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We also recommend that a + file or class name and description of purpose be included on the + same "printed page" as the copyright notice for easier + identification within third-party archives. + + Copyright 2021 SummVis + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. diff --git a/README.md b/README.md new file mode 100644 index 0000000000000000000000000000000000000000..7f8327dc54928e64d00203934dca40ff4210388a --- /dev/null +++ b/README.md @@ -0,0 +1,12 @@ +--- +title: Chapter Summary Summvis +emoji: 📈 +colorFrom: green +colorTo: gray +sdk: streamlit +sdk_version: 1.10.0 +app_file: app.py +pinned: false +--- + +Check out the configuration reference at https://huggingface.co./docs/hub/spaces-config-reference diff --git a/align.py b/align.py new file mode 100644 index 0000000000000000000000000000000000000000..73f4e6b596491d5b29ff0863f9bac42b8a0328ef --- /dev/null +++ b/align.py @@ -0,0 +1,364 @@ +import heapq +import itertools +from abc import ABC, abstractmethod +from collections import defaultdict +from operator import itemgetter +from typing import List, Dict, Tuple +from typing import Sequence +from abc import ABC + +import numpy as np +import torch +from bert_score import BERTScorer +from nltk import PorterStemmer +from spacy.tokens import Doc, Span +from toolz import itertoolz +from transformers import AutoTokenizer +from transformers.tokenization_utils_base import PaddingStrategy + + +class EmbeddingModel(ABC): + @abstractmethod + def embed( + self, + sents: List[Span] + ): + pass + + +class ContextualEmbedding(EmbeddingModel): + + def __init__(self, model, tokenizer_name, max_length, batch_size=32): + self.model = model + self.tokenizer = SpacyHuggingfaceTokenizer(tokenizer_name, max_length) + self._device = model.device + self.batch_size = batch_size + + def embed( + self, + sents: List[Span] + ): + spacy_embs_list = [] + for start_idx in range(0, len(sents), self.batch_size): + batch = sents[start_idx: start_idx + self.batch_size] + encoded_input, special_tokens_masks, token_alignments = self.tokenizer.batch_encode(batch) + encoded_input = {k: v.to(self._device) for k, v in encoded_input.items()} + with torch.no_grad(): + model_output = self.model(**encoded_input) + embeddings = model_output[0].cpu() + for embs, mask, token_alignment \ + in zip(embeddings, special_tokens_masks, token_alignments): + mask = torch.tensor(mask) + embs = embs[mask == 0] # Filter embeddings at special token positions + spacy_embs = [] + for hf_idxs in token_alignment: + if hf_idxs is None: + pooled_embs = torch.zeros_like(embs[0]) + else: + pooled_embs = embs[hf_idxs].mean(dim=0) # Pool embeddings that map to the same spacy token + spacy_embs.append(pooled_embs.numpy()) + spacy_embs = np.stack(spacy_embs) + spacy_embs = spacy_embs / np.linalg.norm(spacy_embs, axis=-1, keepdims=True) # Normalize + spacy_embs_list.append(spacy_embs) + for embs, sent in zip(spacy_embs_list, sents): + assert len(embs) == len(sent) + return spacy_embs_list + + +class StaticEmbedding(EmbeddingModel): + + def embed( + self, + sents: List[Span] + ): + return [ + np.stack([t.vector / (t.vector_norm or 1) for t in sent]) + for sent in sents + ] + + +class Aligner(ABC): + @abstractmethod + def align( + self, + source: Doc, + targets: Sequence[Doc] + ) -> List[Dict]: + """Compute alignment from summary tokens to doc tokens + Args: + source: Source spaCy document + targets: Target spaCy documents + Returns: List of alignments, one for each target document""" + pass + + +class EmbeddingAligner(Aligner): + + def __init__( + self, + embedding: EmbeddingModel, + threshold: float, + top_k: int, + baseline_val=0 + ): + self.threshold = threshold + self.top_k = top_k + self.embedding = embedding + self.baseline_val = baseline_val + + def align( + self, + source: Doc, + targets: Sequence[Doc] + ) -> List[Dict]: + """Compute alignment from summary tokens to doc tokens with greatest semantic similarity + Args: + source: Source spaCy document + targets: Target spaCy documents + Returns: List of alignments, one for each target document + """ + if len(source) == 0: + return [{} for _ in targets] + all_sents = list(source.sents) + list(itertools.chain.from_iterable(target.sents for target in targets)) + chunk_sizes = [_iter_len(source.sents)] + \ + [_iter_len(target.sents) for target in targets] + all_sents_token_embeddings = self.embedding.embed(all_sents) + chunked_sents_token_embeddings = _split(all_sents_token_embeddings, chunk_sizes) + source_sent_token_embeddings = chunked_sents_token_embeddings[0] + source_token_embeddings = np.concatenate(source_sent_token_embeddings) + for token_idx, token in enumerate(source): + if token.is_stop or token.is_punct: + source_token_embeddings[token_idx] = 0 + alignments = [] + for i, target in enumerate(targets): + target_sent_token_embeddings = chunked_sents_token_embeddings[i + 1] + target_token_embeddings = np.concatenate(target_sent_token_embeddings) + for token_idx, token in enumerate(target): + if token.is_stop or token.is_punct: + target_token_embeddings[token_idx] = 0 + alignment = defaultdict(list) + for score, target_idx, source_idx in self._emb_sim_sparse( + target_token_embeddings, + source_token_embeddings, + ): + alignment[target_idx].append((source_idx, score)) + # TODO used argpartition to get nlargest + for j in list(alignment): + alignment[j] = heapq.nlargest(self.top_k, alignment[j], itemgetter(1)) + alignments.append(alignment) + return alignments + + def _emb_sim_sparse(self, embs_1, embs_2): + sim = embs_1 @ embs_2.T + sim = (sim - self.baseline_val) / (1 - self.baseline_val) + keep = sim > self.threshold + keep_idxs_1, keep_idxs_2 = np.where(keep) + keep_scores = sim[keep] + return list(zip(keep_scores, keep_idxs_1, keep_idxs_2)) + + +class BertscoreAligner(EmbeddingAligner): + def __init__( + self, + threshold, + top_k + ): + scorer = BERTScorer(lang="en", rescale_with_baseline=True) + model = scorer._model + embedding = ContextualEmbedding(model, "roberta-large", 510) + baseline_val = scorer.baseline_vals[2].item() + + super(BertscoreAligner, self).__init__( + embedding, threshold, top_k, baseline_val + ) + + +class StaticEmbeddingAligner(EmbeddingAligner): + def __init__( + self, + threshold, + top_k + ): + embedding = StaticEmbedding() + super(StaticEmbeddingAligner, self).__init__( + embedding, threshold, top_k + ) + + +class NGramAligner(Aligner): + + def __init__(self): + self.stemmer = PorterStemmer() + + def align( + self, + source: Doc, + targets: List[Doc], + ) -> List[Dict]: + + alignments = [] + source_ngram_spans = self._get_ngram_spans(source) + for target in targets: + target_ngram_spans = self._get_ngram_spans(target) + alignments.append( + self._align_ngrams(target_ngram_spans, source_ngram_spans) + ) + return alignments + + def _get_ngram_spans( + self, + doc: Doc, + ): + ngrams = [] + for sent in doc.sents: + for n in range(1, len(list(sent))): + tokens = [t for t in sent if not (t.is_stop or t.is_punct)] + ngrams.extend(_ngrams(tokens, n)) + + def ngram_key(ngram): + return tuple(self.stemmer.stem(token.text).lower() for token in ngram) + + key_to_ngrams = itertoolz.groupby(ngram_key, ngrams) + key_to_spans = {} + for k, grouped_ngrams in key_to_ngrams.items(): + key_to_spans[k] = [ + (ngram[0].i, ngram[-1].i + 1) + for ngram in grouped_ngrams + ] + return key_to_spans + + def _align_ngrams( + self, + ngram_spans_1: Dict[Tuple[str], List[Tuple[int, int]]], + ngram_spans_2: Dict[Tuple[str], List[Tuple[int, int]]] + ) -> Dict[Tuple[int, int], List[Tuple[int, int]]]: + """Align ngram spans between two documents + Args: + ngram_spans_1: Map from (normalized_token1, normalized_token2, ...) n-gram tuple to a list of token spans + of format (start_pos, end_pos) + ngram_spans_2: Same format as above, but for second text + Returns: map from each (start, end) span in text 1 to list of aligned (start, end) spans in text 2 + """ + if not ngram_spans_1 or not ngram_spans_2: + return {} + max_span_end_1 = max(span[1] for span in itertools.chain.from_iterable(ngram_spans_1.values())) + token_is_available_1 = [True] * max_span_end_1 # + matched_keys = list(set(ngram_spans_1.keys()) & set(ngram_spans_2.keys())) # Matched normalized ngrams betwee + matched_keys.sort(key=len, reverse=True) # Process n-grams from longest to shortest + + alignment = defaultdict(list) # Map from each matched span in text 1 to list of aligned spans in text 2 + for key in matched_keys: + spans_1 = ngram_spans_1[key] + spans_2 = ngram_spans_2[key] + available_spans_1 = [span for span in spans_1 if all(token_is_available_1[slice(*span)])] + matched_spans_1 = [] + if available_spans_1 and spans_2: + # if ngram can be matched to available spans in both sequences + for span in available_spans_1: + # It's possible that these newly matched spans may be overlapping with one another, so + # check that token positions still available (only one span allowed ber token in text 1): + if all(token_is_available_1[slice(*span)]): + matched_spans_1.append(span) + token_is_available_1[slice(*span)] = [False] * (span[1] - span[0]) + for span1 in matched_spans_1: + alignment[span1] = spans_2 + + return alignment + + +class SpacyHuggingfaceTokenizer: + def __init__( + self, + model_name, + max_length + ): + self.tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=False) + self.max_length = max_length + + def batch_encode( + self, + sents: List[Span] + ): + token_alignments = [] + token_ids_list = [] + + # Tokenize each sentence and special tokens. + for sent in sents: + hf_tokens, token_alignment = self.tokenize(sent) + token_alignments.append(token_alignment) + token_ids = self.tokenizer.convert_tokens_to_ids(hf_tokens) + encoding = self.tokenizer.prepare_for_model( + token_ids, + add_special_tokens=True, + padding=False, + ) + token_ids_list.append(encoding['input_ids']) + + # Add padding + max_length = max(map(len, token_ids_list)) + attention_mask = [] + input_ids = [] + special_tokens_masks = [] + for token_ids in token_ids_list: + encoding = self.tokenizer.prepare_for_model( + token_ids, + padding=PaddingStrategy.MAX_LENGTH, + max_length=max_length, + add_special_tokens=False + ) + input_ids.append(encoding['input_ids']) + attention_mask.append(encoding['attention_mask']) + special_tokens_masks.append( + self.tokenizer.get_special_tokens_mask( + encoding['input_ids'], + already_has_special_tokens=True + ) + ) + + encoded = { + 'input_ids': torch.tensor(input_ids), + 'attention_mask': torch.tensor(attention_mask) + } + return encoded, special_tokens_masks, token_alignments + + def tokenize( + self, + sent + ): + """Convert spacy sentence to huggingface tokens and compute the alignment""" + hf_tokens = [] + token_alignment = [] + for i, token in enumerate(sent): + # "Tokenize" each word individually, so as to track the alignment between spaCy/HF tokens + # Prefix all tokens with a space except the first one in the sentence + if i == 0: + token_text = token.text + else: + token_text = ' ' + token.text + start_hf_idx = len(hf_tokens) + word_tokens = self.tokenizer.tokenize(token_text) + end_hf_idx = len(hf_tokens) + len(word_tokens) + if end_hf_idx < self.max_length: + hf_tokens.extend(word_tokens) + hf_idxs = list(range(start_hf_idx, end_hf_idx)) + else: + hf_idxs = None + token_alignment.append(hf_idxs) + return hf_tokens, token_alignment + + +def _split(data, sizes): + it = iter(data) + return [[next(it) for _ in range(size)] for size in sizes] + + +def _iter_len(it): + return sum(1 for _ in it) + + # TODO set up batching + # To get top K axis and value per row: https://stackoverflow.com/questions/42832711/using-np-argpartition-to-index-values-in-a-multidimensional-array + + +def _ngrams(tokens, n): + for i in range(len(tokens) - n + 1): + yield tokens[i:i + n] diff --git a/app.py b/app.py new file mode 100644 index 0000000000000000000000000000000000000000..a39c2d2a92811625e055eee8a32114b5c67a6f88 --- /dev/null +++ b/app.py @@ -0,0 +1,323 @@ +import argparse +import json +import operator +import os +import re +from pathlib import Path + +import spacy +import spacy.lang.en +import streamlit as st +from meerkat import DataPanel +from spacy.tokens import Doc + +from align import NGramAligner, BertscoreAligner, StaticEmbeddingAligner +from components import MainView +from utils import clean_text + +MIN_SEMANTIC_SIM_THRESHOLD = 0.1 +MAX_SEMANTIC_SIM_TOP_K = 10 + +Doc.set_extension("name", default=None, force=True) +Doc.set_extension("column", default=None, force=True) + + +class Instance(): + def __init__(self, id_, document, reference, preds, data=None): + self.id = id_ + self.document = document + self.reference = reference + self.preds = preds + self.data = data + + +@st.cache(allow_output_mutation=True) +def load_from_index(filename, index): + with open(filename) as f: + for i, line in enumerate(f): + if i == index: + return json.loads(line.strip()) + + +def _nlp_key(x: spacy.Language): + return str(x.path) + + +@st.cache(allow_output_mutation=True, hash_funcs={spacy.lang.en.English: _nlp_key}) +def load_dataset(path: str, nlp: spacy.Language): + if path.endswith('.jsonl'): + return DataPanel.from_jsonl(path) + try: + return DataPanel.read(path, nlp=nlp) + except NotADirectoryError: + return DataPanel.from_jsonl(path) + + +@st.cache(allow_output_mutation=True) +def get_nlp(): + try: + nlp = spacy.load("en_core_web_lg") + except: + nlp = spacy.load("en_core_web_sm") + is_lg = False + else: + is_lg = True + nlp.add_pipe('sentencizer', before="parser") + return nlp, is_lg + + +def retrieve(dataset, index, filename=None): + if index >= len(dataset): + st.error(f"Index {index} exceeds dataset length.") + + eval_dataset = None + if filename: + # TODO Handle this through dedicated fields + if "cnn_dailymail" in filename: + eval_dataset = "cnndm" + elif "xsum" in filename: + eval_dataset = "xsum" + + data = dataset[index] + id_ = data.get('id', '') + + try: + document = data['spacy:document'] + except KeyError: + if not is_lg: + st.error("'en_core_web_lg model' is required unless loading from cached file." + "To install: 'python -m spacy download en_core_web_lg'") + try: + text = data['document'] + except KeyError: + text = data['article'] + if not text: + st.error("Document is blank") + return + document = nlp(text if args.no_clean else clean_text(text)) + document._.name = "Document" + document._.column = "document" + + try: + reference = data['spacy:summary:reference'] + + except KeyError: + if not is_lg: + st.error("'en_core_web_lg model' is required unless loading from cached file." + "To install: 'python -m spacy download en_core_web_lg'") + try: + text = data['summary'] if 'summary' in data else data['summary:reference'] + except KeyError: + text = data.get('highlights') + if text: + reference = nlp(text if args.no_clean else clean_text(text)) + else: + reference = None + if reference is not None: + reference._.name = "Reference" + reference._.column = "summary:reference" + + model_names = set() + for k in data: + m = re.match('(preprocessed_)?summary:(?P.*)', k) + if m: + model_name = m.group('model') + if model_name != 'reference': + model_names.add(model_name) + + preds = [] + for model_name in model_names: + try: + pred = data[f"spacy:summary:{model_name}"] + except KeyError: + if not is_lg: + st.error("'en_core_web_lg model' is required unless loading from cached file." + "To install: 'python -m spacy download en_core_web_lg'") + text = data[f"summary:{model_name}"] + pred = nlp(text if args.no_clean else clean_text(text)) + + parts = model_name.split("-") + primary_sort = 0 + if len(parts) == 2: + model, train_dataset = parts + if train_dataset == eval_dataset: + formatted_model_name = model.upper() + else: + formatted_model_name = f"{model.upper()} ({train_dataset.upper()}-trained)" + if train_dataset in ["xsum", "cnndm"]: + primary_sort = 1 + else: + primary_sort = 2 + else: + formatted_model_name = model_name.upper() + pred._.name = formatted_model_name + pred._.column = f"summary:{model_name}" + preds.append( + ((primary_sort, formatted_model_name), pred) + ) + + preds = [pred for _, pred in sorted(preds)] + + return Instance( + id_=id_, + document=document, + reference=reference, + preds=preds, + data=data, + ) + + +def filter_alignment(alignment, threshold, top_k): + filtered_alignment = {} + for k, v in alignment.items(): + filtered_matches = [(match_idx, score) for match_idx, score in v if score >= threshold] + if filtered_matches: + filtered_alignment[k] = sorted(filtered_matches, key=operator.itemgetter(1), reverse=True)[:top_k] + return filtered_alignment + + +def select_comparison(example): + all_summaries = [] + + if example.reference: + all_summaries.append(example.reference) + if example.preds: + all_summaries.extend(example.preds) + + from_documents = [example.document] + if example.reference: + from_documents.append(example.reference) + document_names = [document._.name for document in from_documents] + select_document_name = sidebar_placeholder_from.selectbox( + label="Comparison FROM:", + options=document_names + ) + document_index = document_names.index(select_document_name) + selected_document = from_documents[document_index] + + remaining_summaries = [summary for summary in all_summaries if + summary._.name != selected_document._.name] + remaining_summary_names = [summary._.name for summary in remaining_summaries] + + selected_summary_names = sidebar_placeholder_to.multiselect( + 'Comparison TO:', + remaining_summary_names, + remaining_summary_names + ) + selected_summaries = [] + for summary_name in selected_summary_names: + summary_index = remaining_summary_names.index(summary_name) + selected_summaries.append(remaining_summaries[summary_index]) + return selected_document, selected_summaries + + +def show_main(example): + # Get user input + + semantic_sim_type = st.sidebar.radio( + "Semantic similarity type:", + ["Contextual embedding", "Static embedding"] + ) + semantic_sim_threshold = st.sidebar.slider( + "Semantic similarity threshold:", + min_value=MIN_SEMANTIC_SIM_THRESHOLD, + max_value=1.0, + step=0.1, + value=0.2, + ) + semantic_sim_top_k = st.sidebar.slider( + "Semantic similarity top-k:", + min_value=1, + max_value=MAX_SEMANTIC_SIM_TOP_K, + step=1, + value=10, + ) + + document, summaries = select_comparison(example) + layout = st.sidebar.radio("Layout:", ["Vertical", "Horizontal"]).lower() + scroll = True + gray_out_stopwords = st.sidebar.checkbox(label="Gray out stopwords", value=True) + + # Gather data + try: + lexical_alignments = [ + example.data[f'{NGramAligner.__name__}:spacy:{document._.column}:spacy:{summary._.column}'] + for summary in summaries + ] + except KeyError: + lexical_alignments = NGramAligner().align(document, summaries) + + if semantic_sim_type == "Static embedding": + try: + semantic_alignments = [ + example.data[f'{StaticEmbeddingAligner.__name__}:spacy:{document._.column}:spacy:{summary._.column}'] + for summary in summaries + ] + except KeyError: + semantic_alignments = StaticEmbeddingAligner( + semantic_sim_threshold, + semantic_sim_top_k).align( + document, + summaries + ) + else: + try: + semantic_alignments = [ + example.data[f'{BertscoreAligner.__name__}:spacy:{document._.column}:spacy:{summary._.column}'] + for summary in summaries + ] + except KeyError: + semantic_alignments = BertscoreAligner(semantic_sim_threshold, + semantic_sim_top_k).align(document, + summaries) + + MainView( + document, + summaries, + semantic_alignments, + lexical_alignments, + layout, + scroll, + gray_out_stopwords, + ).show(height=720) + + +if __name__ == "__main__": + + st.set_page_config(layout="wide") + + parser = argparse.ArgumentParser() + parser.add_argument('--path', type=str, default='data') + parser.add_argument('--no_clean', action='store_true', default=False, + help="Do not clean text (remove extraneous spaces, newlines).") + args = parser.parse_args() + + nlp, is_lg = get_nlp() + + path = Path(args.path) + path_dir = path.parent + all_files = set(map(os.path.basename, path_dir.glob('*'))) + files = sorted([ + fname for fname in all_files if not (fname.endswith(".py") or fname.startswith(".")) + ]) + if path.is_file: + try: + file_index = files.index(path.name) + except: + raise FileNotFoundError(f"File not found: {path.name}") + else: + file_index = 0 + col1, col2 = st.beta_columns((3, 1)) + filename = col1.selectbox(label="File:", options=files, index=file_index) + dataset = load_dataset(str(path_dir / filename), nlp=nlp) + + dataset_size = len(dataset) + query = col2.number_input(f"Index (Size: {dataset_size}):", value=0, min_value=0, max_value=dataset_size - 1) + + sidebar_placeholder_from = st.sidebar.empty() + sidebar_placeholder_to = st.sidebar.empty() + + if query is not None: + example = retrieve(dataset, query, filename) + if example: + show_main(example) diff --git a/components.py b/components.py new file mode 100644 index 0000000000000000000000000000000000000000..a21b73fb02e9ffee9514fad11b57b79008ee8a5d --- /dev/null +++ b/components.py @@ -0,0 +1,567 @@ +from collections import defaultdict +from itertools import count +from operator import itemgetter +from pathlib import Path +from typing import Dict, Optional +from typing import List, Tuple, Union + +import htbuilder +import streamlit as st +from htbuilder import span, div, script, style, link, styles, HtmlElement, br +from htbuilder.units import px +from spacy.tokens import Doc + +palette = [ + "#66c2a5", + "#fc8d62", + "#8da0cb", + "#e78ac3", + "#a6d854", + "#ffd92f", + "#e5c494", + "#b3b3b3", +] +inactive_color = "#BBB" + + +def local_stylesheet(path): + with open(path) as f: + css = f.read() + return style()( + css + ) + + +def remote_stylesheet(url): + return link( + href=url + ) + + +def local_script(path): + with open(path) as f: + code = f.read() + return script()( + code + ) + + +def remote_script(url): + return script( + src=url + ) + + +def get_color(sent_idx): + return palette[sent_idx % len(palette)] + + +def hex_to_rgb(hex): + hex = hex.replace("#", '') + return tuple(int(hex[i:i + 2], 16) for i in (0, 2, 4)) + + +def color_with_opacity(hex_color, opacity): + rgb = hex_to_rgb(hex_color) + return f"rgba({rgb[0]},{rgb[1]},{rgb[2]},{opacity:.2f})" + + +class Component: + + def show(self, width=None, height=None, scrolling=True, **kwargs): + out = div(style=styles( + **kwargs + ))(self.html()) + html = str(out) + st.components.v1.html(html, width=width, height=height, scrolling=scrolling) + + def html(self): + raise NotImplemented + + +class MainView(Component): + + def __init__( + self, + document: Doc, + summaries: List[Doc], + semantic_alignments: Optional[List[Dict]], + lexical_alignments: Optional[List[Dict]], + layout: str, + scroll: bool, + gray_out_stopwords: bool + ): + self.document = document + self.summaries = summaries + self.semantic_alignments = semantic_alignments + self.lexical_alignments = lexical_alignments + self.layout = layout + self.scroll = scroll + self.gray_out_stopwords = gray_out_stopwords + + def html(self): + + # Add document elements + if self.document._.name == 'Document': + document_name = 'Source Document' + else: + document_name = self.document._.name + ' summary' + doc_header = div( + id_="document-header" + )( + document_name + ) + doc_elements = [] + + # Add document content, which comprises multiple elements, one for each summary. Only the elment corresponding to + # selected summary will be visible. + + mu = MultiUnderline() + + for summary_idx, summary in enumerate(self.summaries): + token_idx_to_sent_idx = {} + for sent_idx, sent in enumerate(summary.sents): + for token in sent: + token_idx_to_sent_idx[token.i] = sent_idx + is_selected_summary = (summary_idx == 0) # By default, first summary is selected + + if self.semantic_alignments is not None: + doc_token_idx_to_matches = defaultdict(list) + semantic_alignment = self.semantic_alignments[summary_idx] + for summary_token_idx, matches in semantic_alignment.items(): + for doc_token_idx, sim in matches: + doc_token_idx_to_matches[doc_token_idx].append((summary_token_idx, sim)) + else: + doc_token_idx_to_matches = {} + + token_elements = [] + for doc_token_idx, doc_token in enumerate(self.document): + if doc_token.is_stop or doc_token.is_punct: + classes = ["stopword"] + if self.gray_out_stopwords: + classes.append("grayed-out") + el = span( + _class=" ".join(classes) + )( + doc_token.text + ) + + else: + matches = doc_token_idx_to_matches.get(doc_token_idx) + if matches: + summary_token_idx, sim = max(matches, key=itemgetter(1)) + sent_idx = token_idx_to_sent_idx[summary_token_idx] + color_primary = get_color(sent_idx) + highlight_color_primary = color_with_opacity(color_primary, sim) + props = { + 'data-highlight-id': str(doc_token_idx), + 'data-primary-color': highlight_color_primary + } + match_classes = [] + for summary_token_idx, sim in matches: + sent_idx = token_idx_to_sent_idx[summary_token_idx] + match_classes.append(f"summary-highlight-{summary_idx}-{summary_token_idx}") + color = color_with_opacity(get_color(sent_idx), sim) + props[f"data-color-{summary_idx}-{summary_token_idx}"] = color + props["data-match-classes"] = " ".join(match_classes) + el = self._highlight( + doc_token.text, + highlight_color_primary, + color_primary, + match_classes + ["annotation-hidden"], + **props + ) + else: + el = doc_token.text + token_elements.append(el) + + spans = [] + if self.lexical_alignments is not None: + lexical_alignment = self.lexical_alignments[summary_idx] + for summary_span, doc_spans in lexical_alignment.items(): + summary_span_start, summary_span_end = summary_span + span_id = f"{summary_idx}-{summary_span_start}-{summary_span_end}" + sent_idx = token_idx_to_sent_idx[summary_span_start] + for doc_span_start, doc_span_end in doc_spans: + spans.append(( + doc_span_start, + doc_span_end, + sent_idx, + get_color(sent_idx), + span_id + )) + token_elements = mu.markup(token_elements, spans) + + classes = ["main-doc", "bordered"] + if self.scroll: + classes.append("scroll") + + main_doc = div( + _class=" ".join(classes) + )( + token_elements + ), + + classes = ["doc"] + if is_selected_summary: + classes.append("display") + else: + classes.append("nodisplay") + doc_elements.append( + div( + **{ + "class": " ".join(classes), + "data-index": summary_idx + } + )( + main_doc, + div(_class="proxy-doc"), + div(_class="proxy-scroll") + ) + ) + + summary_title = "Summary" + summary_header = div( + id_="summary-header" + )( + summary_title, + div(id="summary-header-gap"), + ) + + summary_items = [] + for summary_idx, summary in enumerate(self.summaries): + token_idx_to_sent_idx = {} + for sent_idx, sent in enumerate(summary.sents): + for token in sent: + token_idx_to_sent_idx[token.i] = sent_idx + + spans = [] + matches_ngram = [False] * len(list(summary)) + if self.lexical_alignments is not None: + lexical_alignment = self.lexical_alignments[summary_idx] + for summary_span in lexical_alignment.keys(): + start, end = summary_span + matches_ngram[slice(start, end)] = [True] * (end - start) + span_id = f"{summary_idx}-{start}-{end}" + sent_idx = token_idx_to_sent_idx[start] + spans.append(( + start, + end, + sent_idx, + get_color(sent_idx), + span_id + )) + + if self.semantic_alignments is not None: + semantic_alignment = self.semantic_alignments[summary_idx] + else: + semantic_alignment = {} + token_elements = [] + for token_idx, token in enumerate(summary): + if token.is_stop or token.is_punct: + classes = ["stopword"] + if self.gray_out_stopwords: + classes.append("grayed-out") + el = span( + _class=" ".join(classes) + )( + token.text + ) + else: + classes = [] + if token.ent_iob_ in ('I', 'B'): + classes.append("entity") + if matches_ngram[token_idx]: + classes.append("matches-ngram") + matches = semantic_alignment.get(token_idx) + if matches: + top_match = max(matches, key=itemgetter(1)) + top_sim = max(top_match[1], 0) + top_doc_token_idx = top_match[0] + props = { + "data-highlight-id": f"{summary_idx}-{token_idx}", + "data-top-doc-highlight-id": str(top_doc_token_idx), + "data-top-doc-sim": f"{top_sim:.2f}", + } + classes.extend([ + "annotation-hidden", + f"summary-highlight-{summary_idx}-{token_idx}" + ]) + sent_idx = token_idx_to_sent_idx[token_idx] + el = self._highlight( + token.text, + color_with_opacity(get_color(sent_idx), top_sim), + color_with_opacity(get_color(sent_idx), 1), + classes, + **props + ) + else: + if classes: + el = span(_class=" ".join(classes))(token.text) + else: + el = token.text + token_elements.append(el) + + token_elements = mu.markup(token_elements, spans) + + classes = ["summary-item"] + if summary_idx == 0: # Default is for first summary to be selected + classes.append("selected") + + summary_items.append( + div( + **{"class": ' '.join(classes), "data-index": summary_idx} + )( + div(_class="name")(summary._.name), + div(_class="content")(token_elements) + ) + ) + classes = ["summary-list", "bordered"] + if self.scroll: + classes.append("scroll") + if self.lexical_alignments is not None: + classes.append("has-lexical-alignment") + if self.semantic_alignments is not None: + classes.append("has-semantic-alignment") + summary_list = div( + _class=" ".join(classes) + )( + summary_items + ) + + annotation_key = \ + """ + + """ + + body = div( + annotation_key, + div( + _class=f"vis-container {self.layout}-layout" + )( + div( + _class="doc-container" + )( + doc_header, + *doc_elements + ), + div( + _class="summary-container" + )( + summary_header, + summary_list + ) + ), + ) + return [ + """""", + local_stylesheet(Path(__file__).parent / "resources" / "summvis.css"), + """ + """, + body, + """ + """, + local_script(Path(__file__).parent / "resources" / "jquery.color-2.1.2.min.js"), + local_script(Path(__file__).parent / "resources" / "summvis.js"), + """""" + ] + + def _highlight( + self, + token: Union[str, HtmlElement], + background_color, + dotted_underline_color, + classes: List[str], + **props + ): + return span( + _class=" ".join(classes + ["highlight"]), + style=styles( + background_color=background_color, + border_bottom=f"4px dotted {dotted_underline_color}", + ), + **props + )(token) + + +SPACE = " " + + +class MultiUnderline: + def __init__( + self, + underline_thickness=3, + underline_spacing=1 + ): + self.underline_thickness = underline_thickness + self.underline_spacing = underline_spacing + + def markup( + self, + tokens: List[Union[str, HtmlElement]], + spans: List[Tuple[int, int, int, str, str]] + ): + """Style text with multiple layers of colored underlines. + Args: + tokens: list of tokens, either string or html element + spans: list of (start_pos, end_pos, rank, color, id) tuples defined as: + start_pos: start position of underline span + end_pos: end position of underline span + rank: rank for stacking order of underlines, all else being equal + color: color of underline + id: id of underline (encoded as a class label in resulting html element) + Returns: + List of HTML elements + """ + + # Map from span start position to span + start_to_spans = defaultdict(list) + for span in spans: + start = span[0] + start_to_spans[start].append(span) + + # Map from each underline slot position to list of active spans + slot_to_spans = {} + + # Collection of html elements + elements = [] + + first_token_in_line = True + for pos, token in enumerate(tokens): + # Remove spans that are no longer active (end < pos) + slot_to_spans = defaultdict( + list, + { + slot: [span for span in spans if span[1] > pos] # span[1] contains end of spans + for slot, spans in slot_to_spans.items() if spans + } + ) + + # Add underlines to space between tokens for any continuing underlines + if first_token_in_line: + first_token_in_line = False + else: + elements.append(self._get_underline_element(SPACE, slot_to_spans)) + + # Find slot for any new spans + new_spans = start_to_spans.pop(pos, None) + if new_spans: + new_spans.sort( + key=lambda span: (-(span[1] - span[0]), span[2])) # Sort by span length (reversed), rank + for new_span in new_spans: + # Find an existing slot or add a new one + for slot, spans in sorted(slot_to_spans.items(), key=itemgetter(0)): # Sort by slot index + if spans: + containing_span = spans[ + 0] # The first span in the slot strictly contains all other spans + containing_start, containing_end = containing_span[0:2] + containing_color = containing_span[3] + start, end = new_span[0:2] + color = new_span[3] + # If the new span (1) is strictly contained in this span, or (2) exactly matches this span + # and is the same color, then add span to this slot + if end <= containing_end and ( + (start > containing_start or end < containing_end) or + (start == containing_start and end == containing_end and color == containing_color) + ): + spans.append(new_span) + break + else: + # Find a new slot index to add the span + for slot_index in count(): + spans = slot_to_spans[slot_index] + if not spans: # If slot is free, take it + spans.append(new_span) + break + if token in ("\n", "\r", "\r\n"): + elements.append(br()) + first_token_in_line = True + else: + # Add underlines to token for all active spans + elements.append(self._get_underline_element(token, slot_to_spans)) + return elements + + def _get_underline_element(self, token, slot_to_spans): + if not slot_to_spans: + return token + max_slot_index = max(slot_to_spans.keys()) + element = token + for slot_index in range(max_slot_index + 1): + spans = slot_to_spans[slot_index] + if not spans: + color = "rgba(0, 0, 0, 0)" # Transparent element w/opacity=0 + props = {} + else: + containing_slot = spans[0] + color = containing_slot[3] + classes = ["underline"] + if token != SPACE: + classes.append("token-underline") + classes.extend([f"span-{span[4]}" for span in spans]) # Encode ids in class names + props = { + "class": " ".join(classes), + "data-primary-color": color + } + if slot_index == 0: + padding_bottom = 0 + else: + padding_bottom = self.underline_spacing + display = "inline-block" + element = htbuilder.span( + style=styles( + display=display, + border_bottom=f"{self.underline_thickness}px solid", + border_color=color, + padding_bottom=px(padding_bottom), + ), + **props + )(element) + + # Return outermost nested span + return element + + +if __name__ == "__main__": + from htbuilder import div + + # Test + text = "The quick brown fox jumps" + tokens = text.split() + tokens = [ + "The", + htbuilder.span(style=styles(color="red"))("quick"), + "brown", + "fox", + "jumps" + ] + spans = [ + (0, 2, 0, "green", "green1"), + (1, 3, 0, "orange", "orange1"), + (3, 4, 0, "red", "red1"), + (2, 4, 0, "blue", "blue1"), + (1, 5, 0, "orange", "orange1"), + ] + + mu = MultiUnderline() + html = str(div(mu.markup(tokens, spans))) + print(html) diff --git a/examples/booksum/booksum_sf_sample.jsonl b/examples/booksum/booksum_sf_sample.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..dba495a1d3e61b7a92b7d45999860119b42da7a3 --- /dev/null +++ b/examples/booksum/booksum_sf_sample.jsonl @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:480c9b9d1e401075aa1fc17d9409f899fc01462202b8688da1cdef8a7d7fef94 +size 102823 diff --git a/examples/booksum/booksum_sf_sample_processed.cache/meta.yaml b/examples/booksum/booksum_sf_sample_processed.cache/meta.yaml new file mode 100644 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a/examples/wikinews/wikinews.cache/mgr/columns/summary:pegasus-cnndm/state.dill b/examples/wikinews/wikinews.cache/mgr/columns/summary:pegasus-cnndm/state.dill new file mode 100644 index 0000000000000000000000000000000000000000..aa2e52184526d9e7a71b0a5f006e2717e8c5dc03 --- /dev/null +++ b/examples/wikinews/wikinews.cache/mgr/columns/summary:pegasus-cnndm/state.dill @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:99bcd77ed9facb87e01faaa0a8ab2195bea78fcee733ce044e96e9e290510797 +size 87 diff --git a/examples/wikinews/wikinews.cache/mgr/columns/summary:pegasus-xsum/state.dill b/examples/wikinews/wikinews.cache/mgr/columns/summary:pegasus-xsum/state.dill new file mode 100644 index 0000000000000000000000000000000000000000..aa2e52184526d9e7a71b0a5f006e2717e8c5dc03 --- /dev/null +++ b/examples/wikinews/wikinews.cache/mgr/columns/summary:pegasus-xsum/state.dill @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:99bcd77ed9facb87e01faaa0a8ab2195bea78fcee733ce044e96e9e290510797 +size 87 diff --git a/examples/wikinews/wikinews.cache/mgr/columns/summary:reference/state.dill b/examples/wikinews/wikinews.cache/mgr/columns/summary:reference/state.dill new file mode 100644 index 0000000000000000000000000000000000000000..aa2e52184526d9e7a71b0a5f006e2717e8c5dc03 --- /dev/null +++ b/examples/wikinews/wikinews.cache/mgr/columns/summary:reference/state.dill @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:99bcd77ed9facb87e01faaa0a8ab2195bea78fcee733ce044e96e9e290510797 +size 87 diff --git a/examples/wikinews/wikinews.cache/mgr/meta.yaml b/examples/wikinews/wikinews.cache/mgr/meta.yaml new file mode 100644 index 0000000000000000000000000000000000000000..5542890a1191d57612d749fbc515b6708a48a57f --- /dev/null +++ b/examples/wikinews/wikinews.cache/mgr/meta.yaml @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:db75b2d90735d7895a6f340ea99717442be61c14db11af4915323a592a1b7b85 +size 7383 diff --git a/examples/wikinews/wikinews.cache/state.dill b/examples/wikinews/wikinews.cache/state.dill new file mode 100644 index 0000000000000000000000000000000000000000..d00002db246d889528cc3291da42db3fe20a1a9f --- /dev/null +++ b/examples/wikinews/wikinews.cache/state.dill @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:8ec16a78d9c9d3646c559df239aea3f15333605ddc702eeacebf6dd3ebeff684 +size 6 diff --git a/examples/wikinews/wikinews.jsonl b/examples/wikinews/wikinews.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..e082f944768ee35e21a806b285925ebdc8d4dc3c --- /dev/null +++ b/examples/wikinews/wikinews.jsonl @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:5656cb3b410b745a5c75d7a09c9a6e3f1fd74d8a94615c7a5122fd0f730ed48b +size 1736 diff --git a/examples/xsum/load.sh b/examples/xsum/load.sh new file mode 100644 index 0000000000000000000000000000000000000000..f495956adc0b0392556db7334a57d154ec2d69ef --- /dev/null +++ b/examples/xsum/load.sh @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:eaf516e3fda6a153e00051d0894c323718b360ce4dbed52dcce02ba03d642bb9 +size 1096 diff --git a/generation.py b/generation.py new file mode 100644 index 0000000000000000000000000000000000000000..b1ef93335a48631d95722b7ce70912cd77213998 --- /dev/null +++ b/generation.py @@ -0,0 +1,106 @@ +""" +Script for decoding summarization models available through Huggingface Transformers. + +To use with one of the 6 standard models: +python generation.py --model --data_path + where model abbreviation is one of: bart-xsum, bart-cnndm, pegasus-xsum, pegasus-cnndm, pegasus-newsroom, + pegasus-multinews: + +To use with arbitrary model: +python generation.py --model_name_or_path --data_path + +""" +# !/usr/bin/env python +# coding: utf-8 + +import argparse +import json +import os + +import torch +from tqdm import tqdm +from transformers import AutoModelForSeq2SeqLM, AutoTokenizer + +BATCH_SIZE = 8 +DEVICE = 'cuda' if torch.cuda.is_available() else 'cpu' + +BART_CNNDM_CHECKPOINT = 'facebook/bart-large-cnn' +BART_XSUM_CHECKPOINT = 'facebook/bart-large-xsum' +PEGASUS_CNNDM_CHECKPOINT = 'google/pegasus-cnn_dailymail' +PEGASUS_XSUM_CHECKPOINT = 'google/pegasus-xsum' +PEGASUS_NEWSROOM_CHECKPOINT = 'google/pegasus-newsroom' +PEGASUS_MULTINEWS_CHECKPOINT = 'google/pegasus-multi_news' + +MODEL_CHECKPOINTS = { + 'bart-xsum': BART_XSUM_CHECKPOINT, + 'bart-cnndm': BART_CNNDM_CHECKPOINT, + 'pegasus-xsum': PEGASUS_XSUM_CHECKPOINT, + 'pegasus-cnndm': PEGASUS_CNNDM_CHECKPOINT, + 'pegasus-newsroom': PEGASUS_NEWSROOM_CHECKPOINT, + 'pegasus-multinews': PEGASUS_MULTINEWS_CHECKPOINT +} + + +class JSONDataset(torch.utils.data.Dataset): + def __init__(self, data_path): + super(JSONDataset, self).__init__() + + with open(data_path) as fd: + self.data = [json.loads(line) for line in fd] + + def __len__(self): + return len(self.data) + + def __getitem__(self, idx): + return self.data[idx] + + +def postprocess_data(decoded): + """ + Remove generation artifacts and postprocess outputs + + :param decoded: model outputs + """ + return [x.replace('', ' ') for x in decoded] + + +if __name__ == '__main__': + parser = argparse.ArgumentParser(description='Process some integers.') + parser.add_argument('--model', type=str) + parser.add_argument('--model_name_or_path', type=str) + parser.add_argument('--data_path', type=str) + args = parser.parse_args() + + if not (args.model or args.model_name_or_path): + raise ValueError('Model is required') + + if args.model and args.model_name_or_path: + raise ValueError('Specify model or model_name_or_path but not both') + + # Load models & data + if args.model: + model_name_or_path = MODEL_CHECKPOINTS[args.model] + file_model_name = args.model + else: + model_name_or_path = args.model_name_or_path + file_model_name = model_name_or_path.replace("/", "-") + model = AutoModelForSeq2SeqLM.from_pretrained(model_name_or_path).to(DEVICE) + tokenizer = AutoTokenizer.from_pretrained(model_name_or_path) + + dataset = JSONDataset(args.data_path) + + dataloader = torch.utils.data.DataLoader(dataset, batch_size=BATCH_SIZE) + + # Write out dataset + file_dataset_name = os.path.splitext(os.path.basename(args.data_path))[0] + filename = f'{file_model_name}.{file_dataset_name}.predictions' + fd_out = open(filename, 'w') + + model.eval() + with torch.no_grad(): + for raw_data in tqdm(dataloader): + batch = tokenizer(raw_data["document"], return_tensors="pt", truncation=True, padding="longest").to(DEVICE) + summaries = model.generate(input_ids=batch.input_ids, attention_mask=batch.attention_mask) + decoded = tokenizer.batch_decode(summaries, skip_special_tokens=True, clean_up_tokenization_spaces=False) + for example in postprocess_data(decoded): + fd_out.write(example + '\n') diff --git a/join.py b/join.py new file mode 100644 index 0000000000000000000000000000000000000000..8610f4036e0cdfb3d559dd936893aef086b3f495 --- /dev/null +++ b/join.py @@ -0,0 +1,80 @@ +""" +Script for joining dataset of documents/reference summaries with generated summaries (likely from generate.py). + +Usage with custom datasets in JSONL format: +python join.py --data_path --generation_paths --output_path + +Optionally specify --model_names to override default model names. + +""" +# !/usr/bin/env python +# coding: utf-8 + +import argparse +import json +import os +from pathlib import Path + +import torch +from tqdm import tqdm + +BATCH_SIZE = 8 + + +class JSONDataset(torch.utils.data.Dataset): + def __init__(self, data_path): + super(JSONDataset, self).__init__() + + with open(data_path) as fd: + self.data = [json.loads(line) for line in fd] + + def __len__(self): + return len(self.data) + + def __getitem__(self, idx): + return self.data[idx] + + +if __name__ == '__main__': + parser = argparse.ArgumentParser() + parser.add_argument('--data_path', type=str) + parser.add_argument('--generation_paths', type=str, nargs="+", required=True) + parser.add_argument('--output_path', type=str, required=True) + parser.add_argument('--model_names', type=str, nargs="+") + args = parser.parse_args() + + if args.model_names and len(args.generation_paths) != len(args.model_names): + raise ValueError('Length of args.generation_paths must equal length of args.model_names') + + if args.model_names: + model_names = args.model_names + else: + model_names = [Path(p).name.split(".")[0] for p in args.generation_paths] + + args.dataset = os.path.splitext(os.path.basename(args.data_path))[0] + args.split = 'user' + + # Load data + + dataset = JSONDataset(args.data_path) + + # Join files and write out single jsonl dataset + + generation_files = [open(fname) for fname in args.generation_paths] + + with open(args.output_path, 'w') as outp: + for row in tqdm(zip(dataset, *generation_files)): + # Process each original data record in parallel with generation(s) of the model(s) + result = {} + data = row[0] + generations = row[1:] + result['summary:reference'] = data['summary:reference'] + result['document'] = data['document'] + for model_name, gen in zip(model_names, generations): + result[f'summary:{model_name}'] = gen + outp.write( + json.dumps(result) + '\n' + ) + + for file in generation_files: + file.close() diff --git a/preprocessing.py b/preprocessing.py new file mode 100644 index 0000000000000000000000000000000000000000..934ecfa6fc1f61950c674e3656cfc9a003275e69 --- /dev/null +++ b/preprocessing.py @@ -0,0 +1,343 @@ +import logging +from argparse import ArgumentParser +from typing import List + +from meerkat import DataPanel, SpacyColumn +from meerkat.logging.utils import set_logging_level +from spacy import load + +from align import BertscoreAligner, NGramAligner, StaticEmbeddingAligner, Aligner +from utils import clean_text + +set_logging_level('critical') +logger = logging.getLogger(__name__) +logger.setLevel(logging.CRITICAL) + + +def _run_aligners( + dataset: DataPanel, + aligners: List[Aligner], + doc_column: str, + reference_column: str, + summary_columns: List[str] = None, +): + if not summary_columns: + summary_columns = [] + + to_columns = [] + if reference_column is not None: + to_columns.append(reference_column) + to_columns.extend(summary_columns) + + for aligner in aligners: + + # Run the aligner on (document, summary) pairs + dataset = dataset.update( + lambda x: { + f'{type(aligner).__name__}:{doc_column}:{to_columns}': + aligner.align( + x[doc_column], + [x[col] for col in to_columns], + ), + }, + ) + + if reference_column is not None and len(summary_columns): + # Run the aligner on (reference, summary) pairs + dataset = dataset.update( + lambda x: { + f'{type(aligner).__name__}:{reference_column}:{summary_columns}': aligner.align( + x[reference_column], + [x[col] for col in summary_columns], + ), + }, + ) + + if len(to_columns) > 1: + # Instead of having one column for (document, summary) comparisons, split + # off into (1 + |summary_columns|) total columns, one for each comparison + + # Retrieve the (document, summary) column + doc_summary_column = dataset[f'{type(aligner).__name__}:{doc_column}:{to_columns}'] + + for i, col in enumerate(to_columns): + # Add as a new column after encoding with the aligner's `encode` method + dataset.add_column( + f'{type(aligner).__name__}:{doc_column}:{col}', + [row[i] for row in doc_summary_column], + ) + + # Remove the (document, summary) column + dataset.remove_column(f'{type(aligner).__name__}:{doc_column}:{to_columns}') + + if reference_column is not None and len(summary_columns) > 1: + # Instead of having one column for (reference, summary) comparisons, split + # off into (|summary_columns|) total columns, one for each comparison + + # Retrieve the (reference, summary) column + reference_summary_column = dataset[f'{type(aligner).__name__}:{reference_column}:{summary_columns}'] + + for i, col in enumerate(summary_columns): + # Add as a new column + dataset.add_column( + f'{type(aligner).__name__}:{reference_column}:{col}', + [row[i] for row in reference_summary_column], + ) + + # Remove the (reference, summary) column + dataset.remove_column(f'{type(aligner).__name__}:{reference_column}:{summary_columns}') + + return dataset + + +def load_nlp(): + try: + return load('en_core_web_lg') + except OSError: + raise OSError("'en_core_web_lg model' is required unless loading from cached file." + "To install: 'python -m spacy download en_core_web_lg'") + + +def run_workflow( + jsonl_path: str, + doc_column: str = None, + reference_column: str = None, + summary_columns: List[str] = None, + bert_aligner_threshold: float = 0.5, + bert_aligner_top_k: int = 3, + embedding_aligner_threshold: float = 0.5, + embedding_aligner_top_k: int = 3, + processed_dataset_path: str = None, + n_samples: int = None +): + if not jsonl_path: + raise ValueError("'jsonl_path' is required") + + if not processed_dataset_path: + raise ValueError("Please specify a path to save the dataset.") + + # Load the dataset + dataset = DataPanel.from_jsonl(jsonl_path) + + if doc_column is None: + # Assume `doc_column` is called "document" + doc_column = 'document' + assert doc_column in dataset.columns, \ + f"`doc_column={doc_column}` is not a column in datapanel." + print("Assuming `doc_column` is called 'document'.") + + if reference_column is None: + # Assume `reference_column` is called "summary:reference" + reference_column = 'summary:reference' + print("Assuming `reference_column` is called 'summary:reference'.") + if reference_column not in dataset.columns: + print("No reference summary loaded") + reference_column = None + + if summary_columns is None or len(summary_columns) == 0: + # Assume `summary_columns` are prefixed by "summary:" + summary_columns = [] + for col in dataset.columns: + if col.startswith("summary:") and col != "summary:reference": + summary_columns.append(col) + print(f"Reading summary columns from datapanel. Found {summary_columns}.") + + if len(summary_columns) == 0 and reference_column is None: + raise ValueError("At least one summary is required") + + # Restrict to the first `n_samples` + if n_samples: + print(f"Restricting to {n_samples} samples.") + dataset = dataset.head(n_samples) + + print("size of dataset:", len(dataset)) + + # Combine the text columns into one list + text_columns = [doc_column] + ([reference_column] if reference_column else []) + summary_columns + + # Preprocessing all the text columns + print("Preprocessing text columns") + dataset = dataset.update( + lambda x: { + f'preprocessed_{k}': x[k] if args.no_clean else clean_text(x[k]) + for k in text_columns + } + ) + + # Run the Spacy pipeline on all preprocessed text columns + nlp = load_nlp() + + nlp.add_pipe('sentencizer', before="parser") + + print("Running spacy processing") + for col in text_columns: + dataset.add_column(f'spacy:{col}', SpacyColumn.from_docs(nlp.pipe(dataset[f'preprocessed_{col}']))) + + # Run the 3 align pipelines + bert_aligner = BertscoreAligner( + threshold=bert_aligner_threshold, + top_k=bert_aligner_top_k, + ) + + embedding_aligner = StaticEmbeddingAligner( + threshold=embedding_aligner_threshold, + top_k=embedding_aligner_top_k, + ) + + ngram_aligner = NGramAligner() + + dataset = _run_aligners( + dataset=dataset, + aligners=[bert_aligner, embedding_aligner, ngram_aligner], + doc_column=f'spacy:{doc_column}', + reference_column=f'spacy:{reference_column}' if reference_column else None, + summary_columns=[f'spacy:{col}' for col in summary_columns], + ) + + # Save the dataset + dataset.write(processed_dataset_path) + + return dataset + + +def standardize_dataset( + dataset_name: str, + dataset_version: str, + dataset_split: str, + save_jsonl_path: str, + doc_column: str = None, + reference_column: str = None, + n_samples: int = None + +): + """Load a dataset from Huggingface and dump it to disk.""" + + if args.dataset is None or \ + args.split is None or \ + args.save_jsonl_path is None: + raise ValueError('Missing command line argument') + + # Load the dataset from Huggingface + dataset = get_dataset( + dataset_name=dataset_name, + dataset_version=dataset_version, + dataset_split=dataset_split + ) + if n_samples: + dataset = dataset[:n_samples] + + if doc_column is None: + if reference_column is not None: + raise ValueError("You must specify `doc_column` if you specify `reference_column`") + try: + doc_column, reference_column = { + 'cnn_dailymail': ('article', 'highlights'), + 'xsum': ('document', 'summary') + }[dataset_name] + except: + raise NotImplementedError( + "Please specify `doc_column`." + ) + + # Rename the columns + if doc_column != 'document': + dataset.add_column('document', dataset[doc_column]) + dataset.remove_column(doc_column) + dataset.add_column('summary:reference', dataset[reference_column]) + dataset.remove_column(reference_column) + + # Save the dataset back to disk + dataset.to_jsonl(save_jsonl_path) + return dataset + + +def get_dataset( + dataset_name: str = None, + dataset_version: str = None, + dataset_split: str = 'test', + dataset_jsonl: str = None, +): + """Load a dataset.""" + assert (dataset_name is not None) != (dataset_jsonl is not None), \ + "Specify one of `dataset_name` or `dataset_jsonl`." + + # Load the dataset + if dataset_name is not None: + return get_hf_dataset(dataset_name, dataset_version, dataset_split) + + return DataPanel.from_jsonl(json_path=dataset_jsonl) + + +def get_hf_dataset(name: str, version: str = None, split: str = 'test'): + """Get dataset from Huggingface.""" + if version: + return DataPanel.from_huggingface(name, version, split=split) + return DataPanel.from_huggingface(name, split=split) + + +if __name__ == '__main__': + parser = ArgumentParser() + parser.add_argument('--dataset', type=str, choices=['cnn_dailymail', 'xsum'], + help="Huggingface dataset name.") + parser.add_argument('--version', type=str, + help="Huggingface dataset version.") + parser.add_argument('--split', type=str, default='test', + help="Huggingface dataset split.") + parser.add_argument('--dataset_jsonl', type=str, + help="Path to a jsonl file for the dataset.") + parser.add_argument('--save_jsonl_path', type=str, + help="Path to save the processed jsonl dataset.") + parser.add_argument('--doc_column', type=str, + help="Name of the document column in the dataset.") + parser.add_argument('--reference_column', type=str, + help="Name of the reference summary column in the dataset.") + parser.add_argument('--summary_columns', nargs='+', default=[], + help="Name of other summary columns in/added to the dataset.") + + parser.add_argument('--bert_aligner_threshold', type=float, default=0.1, + help="Minimum threshold for BERT alignment.") + parser.add_argument('--bert_aligner_top_k', type=int, default=10, + help="Top-k for BERT alignment.") + parser.add_argument('--embedding_aligner_threshold', type=float, default=0.1, + help="Minimum threshold for embedding alignment.") + parser.add_argument('--embedding_aligner_top_k', type=int, default=10, + help="Top-k for embedding alignment.") + parser.add_argument('--processed_dataset_path', type=str, + help="Path to store the final processed dataset.") + parser.add_argument('--n_samples', type=int, + help="Number of dataset samples to process.") + + parser.add_argument('--workflow', action='store_true', default=False, + help="Whether to run the preprocessing workflow.") + parser.add_argument('--standardize', action='store_true', default=False, + help="Whether to standardize the dataset and save to jsonl.") + parser.add_argument('--no_clean', action='store_true', default=False, + help="Do not clean text (remove extraneous spaces, newlines).") + args = parser.parse_args() + + if args.standardize: + # Dump a Huggingface dataset to standardized jsonl format + standardize_dataset( + dataset_name=args.dataset, + dataset_version=args.version, + dataset_split=args.split, + save_jsonl_path=args.save_jsonl_path, + doc_column=args.doc_column, + reference_column=args.reference_column, + n_samples=args.n_samples + ) + + if args.workflow: + # Run the processing workflow + run_workflow( + jsonl_path=args.dataset_jsonl, + doc_column=args.doc_column, + reference_column=args.reference_column, + summary_columns=args.summary_columns, + bert_aligner_threshold=args.bert_aligner_threshold, + bert_aligner_top_k=args.bert_aligner_top_k, + embedding_aligner_threshold=args.embedding_aligner_threshold, + embedding_aligner_top_k=args.embedding_aligner_top_k, + processed_dataset_path=args.processed_dataset_path, + n_samples=args.n_samples + ) diff --git a/quickstart.sh b/quickstart.sh new file mode 100644 index 0000000000000000000000000000000000000000..8407c22014c1319e4dfba065caf4d9a5755dbab3 --- /dev/null +++ b/quickstart.sh @@ -0,0 +1,12 @@ +mkdir -p data && +mkdir -p preprocessing && +curl https://storage.googleapis.com/sfr-summvis-data-research/cnn_dailymail_1000.validation.anonymized.zip > preprocessing/cnn_dailymail_1000.validation.anonymized.zip && +unzip -o preprocessing/cnn_dailymail_1000.validation.anonymized.zip -d preprocessing/ && +python preprocessing.py \ +--deanonymize \ +--dataset_rg preprocessing/cnn_dailymail_1000.validation.anonymized \ +--dataset cnn_dailymail \ +--version 3.0.0 \ +--split validation \ +--processed_dataset_path data/cnn_dailymail_10.validation \ +--n_samples 10 \ No newline at end of file diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..a0f71ba72aa46243712350b9df3606c4c726fcf8 --- /dev/null +++ b/requirements.txt @@ -0,0 +1,15 @@ +# environment.yml must be kept in sync +spacy==3.0.3 +streamlit==0.77.0 +st-annotated-text==1.1.0 +transformers +datasets==1.18.4 +torch>=1.8.0,<2.0.0 +bert-score==0.3.7 +rouge-score==0.0.4 +toolz==0.11.1 +nltk==3.4.5 +meerkat-ml==0.2.4 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diff --git a/resources/summvis.css b/resources/summvis.css new file mode 100644 index 0000000000000000000000000000000000000000..9e3e3f506e13d0d05898f7c40e70f046c10a5019 --- /dev/null +++ b/resources/summvis.css @@ -0,0 +1,347 @@ +body { + font-family: 'Roboto', sans-serif; + font-weight: 400; + line-height: 1.5; + color: #262730; + font-weight: 400; +} + +.vis-container { + height: 670px; + background-color: #F5F7F9; +} + +.nodisplay { + display: none !important; +} + +.scroll { + overflow-y: scroll; +} + +.doc-container { + padding: 10px 20px; +} + +.horizontal-layout .doc-container { + padding-bottom: 0px; +} + +.vertical-layout .doc-container { + float: left; + width: 50%; + padding-right: 0px; +} + +.summary-container { + padding: 0px 20px; +} + +.vertical-layout .summary-container { + float: left; + width: 50%; + padding-top: 8px; +} + +.vertical-layout .main-doc.scroll { + height: 610px; +} + +.main-doc.scroll { + scrollbar-width: none; +} + +/* Works on Chrome, Edge, and Safari */ +.main-doc.scroll::-webkit-scrollbar { + width: 0; +} + +.vertical-layout .proxy-doc { + height: 610px; +} + +.vertical-layout .summary-list.scroll { + height: 610px; +} + +.horizontal-layout .scroll { + height: 270px; +} + +.doc { + display: flex; +} + +.horizontal-layout .doc { +} + +.main-doc { + background-color: white; + padding-left: 17px; + padding-right: 15px; + padding-top: 16px; + border-top-left-radius: 4px; + border-bottom-left-radius: 4px; + flex: 1; + border: 1px solid #e9e9e9; +} + +.display .proxy-scroll { + position: absolute; + left: 9px; + width: 9px; + border-radius: 6px; + background-color: rgba(0, 0, 0, 0.1); +} + +.display .proxy-scroll.hover { + background-color: rgba(0, 0, 0, 0.2); +} + +.proxy-doc { + flex: 0 0 28px; + background-color: white; + position: relative; + border-bottom-right-radius: 4px; + border-top-right-radius: 4px; + padding-left: 3px; + padding-right: 3px; + border-top: 1px solid #e9e9e9; + border-right: 1px solid #e9e9e9; + border-bottom: 1px solid #e9e9e9; +} + +.vertical-layout .proxy-doc { + margin-right: 25px; +} + +.summary-list { + border-top: 1px solid #ccc; + border-bottom: 1px solid #ccc; + border-radius: 4px; +} + +.summary-item { + border-bottom: 1px solid #ccc; + border-left: 1px solid #ccc; + border-right: 1px solid #ccc; + background-color: white; + padding-top: 16px; + padding-bottom: 16px; + padding-left: 23px; + padding-right: 8px; +} + +.summary-item:last-child { + border-bottom: 0px; + border-bottom-left-radius: 3px; +} + +.summary-item.selected.selectable { + border-left: 3px solid #2377E9; + padding-left: 21px; +} + +.summary-item.selectable:not(.selected):hover { + cursor: pointer; + background-color: #FCFDFF; +} + +.summary-item.selected.selectable .highlight:not(.annotation-hidden):hover { + cursor: pointer; +} + +.summary-item.selected.selectable .underline:not(.annotation-hidden):hover { + cursor: pointer; +} + +.summary-item .name { + margin-bottom: 8px; + font-weight: 400; +} + +.summary-item.selected.selectable .name { + font-weight: 500; +} + +.inactive { + opacity: 0.5 !important; +} + +.stopword.grayed-out { + opacity: 50% +} + +.has-lexical-alignment .annotate-novel { + /* Bold all non-underlined items */ + font-weight: 500; + color: black; +} + +.summary-item .stopword { + font-weight: 400; +} + +.summary-item .token-underline { + font-weight: 400; +} + +.summary-item:not(.selected) .underline, .summary-item:not(.selectable) .underline { + border-color: #909090 !important; +} + +.underline.annotation-inactive { + border-color: #E9E9E9 !important; +} + +.underline.annotation-invisible { + border-color: transparent !important; +} + +.underline.annotation-hidden { + border: 0px !important; + margin: 0px !important; +} + +.proxy-underline.annotation-hidden, .proxy-highlight.annotation-hidden { + visibility: hidden; +} + +.proxy-underline.annotation-inactive { + background-color: #E9E9E9 !important; +} + +.proxy-underline.annotation-invisible { + background-color: transparent !important; +} + +.highlight { + display: inline-block; +} + +.highlight.annotation-hidden { + background: none !important; + border-color: transparent !important; + border-bottom: 0px !important; +} + +.highlight.annotation-invisible { + background-color: transparent !important; + border-color: transparent !important; +} + +.summary-item:not(.selected) .highlight:not(.annotation-hidden), +.summary-item:not(.selectable) .highlight:not(.annotation-hidden) { + border-color: #909090 !important; +} + +.highlight.annotation-inactive { + border-color: #E9E9E9 !important; +} + +.display .proxy-scroll.hidden { + visibility: hidden; +} + +#document-header { + min-height: 35px; + margin-bottom: 0px; + align-items: center; + color: black; + display: flex; +} + +#summary-header { + display: flex; + justify-content: space-between; + align-items: center; + min-height: 35px; + margin-bottom: 0px; + color: black; +} + +.horizontal-layout #summary-header { + margin-top: 23px; +} + +#summary-header-gap { + flex: 1 0 15px; +} + +.highlight.selected { + border-color: transparent !important; +} + +.highlight:not(.selected), .proxy-highlight:not(.selected) { + background-color: transparent !important; +} + +.summary-item.annotate-entities .entity:not(.matches-ngram) { + color: #fb425c; + font-weight: 500; +} + +.summary-item.annotate-lexical .highlight.matches-ngram { + padding: 0px; + border-bottom: 0px !important; +} + +.doc .highlight { + padding: 0px; + border: 0px !important; +} + +ul.annotation-key { + display: flex; + align-items: flex-end; + list-style: none; + justify-content: flex-start; + padding: 0px; + margin: 0px 0px 10px 0px; +} + +.annotation-key li { + margin-right: 15px; + font-size: 13px; + padding: 6px 13px 6px 13px; +} + +.annotation-key li.option { + border-radius: 13px; + cursor: pointer; + border: 1px solid #F3F3F3; +} + +.annotation-key li.option.selected { + background-color: #F0F2F6; +} + +.annotation-key-label { + margin: 0px; + padding-left: 0px !important; + padding-right: 0px !important; +} + +.annotation-key-ngram { + border-bottom: 3px solid #66c2a5; + padding-bottom: 1px; +} + +.annotation-key-semantic { + border-bottom: 4px dotted #66c2a5; + padding-bottom: 1px; +} + +.annotation-key-novel { + font-weight: 500; + color: black; +} + +.annotation-key-entity { + font-weight: 500; + color: #fb425c; +} + +.annotation-key-stopword { + opacity: 70%; +} diff --git a/resources/summvis.js b/resources/summvis.js new file mode 100644 index 0000000000000000000000000000000000000000..dbce298d922226a7510a1ab6270281485cd4a12b --- /dev/null +++ b/resources/summvis.js @@ -0,0 +1,518 @@ +$(document).ready( + function () { + + // Define global variables + + let isDragging = false; + let saveDragPos; + + let rtime; + let timeout = false; + let delta = 200; + + let disableScrollEvent = false; + + let annotateLexical = false; + let annotateSemantic = false; + let annotateNovel = false; + let annotateEntities = false; + + // Define functions + + function clamp(number, min, max) { + return Math.max(min, Math.min(number, max)); + } + + function hasScroll() { + const el = $(".display .main-doc"); + return el.prop("scrollHeight") > el.prop("clientHeight"); + } + + function scrollBy(delta) { + const proxyDoc = $(".display .proxy-doc"); + const proxyScroll = proxyDoc.find(".proxy-scroll"); + const currentTop = parseFloat(proxyScroll.css("top")); + const newTop = clamp(currentTop + delta, 0, proxyDoc.innerHeight() - proxyScroll.innerHeight()); + proxyScroll.css("top", newTop); + const mainDoc = $(".display .main-doc"); + const scaleY = mainDoc[0].scrollHeight / proxyDoc.innerHeight(); + mainDoc.scrollTop(newTop * scaleY) + } + + function getSpanId(el) { + return getSpanIds(el)[0] + } + + function getSpanIds(el) { + return el.attr("class").split(/\s+/).filter(function (x) { + return x.startsWith("span-") + }); + } + + function createProxy() { + const mainDoc = $(".display .main-doc"); + const proxyDoc = $(".display .proxy-doc"); + const proxyHeight = proxyDoc.innerHeight(); + const proxyWidth = proxyDoc.innerWidth(); + const scaleX = 0.8 * proxyWidth / mainDoc.innerWidth(); + const scaleY = proxyHeight / mainDoc[0].scrollHeight; + const scrollTop = mainDoc.scrollTop(); + const proxyScrollTop = scrollTop * scaleY; + const proxyScrollBottom = (scrollTop + mainDoc.innerHeight()) * scaleY; + const proxyScrollHeight = proxyScrollBottom - proxyScrollTop; + proxyDoc.empty(); + + // Loop through underlines in doc view and create associated proxy element + if (annotateLexical) { + $(".display .main-doc .token-underline").each( + function (index, value) { + const el = $(value); + const x = el.position().left; + const y = mainDoc.scrollTop() + el.position().top - mainDoc.position().top; + const newHeight = 3; + const color = el.css("border-bottom-color"); + const proxyPadding = proxyDoc.innerWidth() - proxyDoc.width(); + const newX = x * scaleX + proxyPadding / 2; + const newY = (y + el.height()) * scaleY - newHeight; + const newWidth = Math.min( + Math.max((el.width() * scaleX) + 1, 5), + proxyDoc.width() + proxyPadding / 2 - newX + ); + + let classes = "proxy-underline annotation-hidden " + getSpanIds(el).join(" "); + const proxyEl = $('
', { + "class": classes, + "css": { + "position": "absolute", + "left": Math.round(newX), + "top": Math.round(newY), + "background-color": color, + "width": newWidth, + "height": newHeight, + } + }).appendTo(proxyDoc); + proxyEl.data(el.data()); + } + ); + } + + // Loop through all active highlights in doc view and create associated proxy element + if (annotateSemantic) { + $(".display .main-doc .highlight").each( + function (index, value) { + const el = $(value); + const x = el.position().left; + const y = mainDoc.scrollTop() + el.position().top - mainDoc.position().top; + const newHeight = 5; + const color = el.css("background-color"); + const proxyPadding = proxyDoc.innerWidth() - proxyDoc.width() + const newX = x * scaleX + proxyPadding / 2; + const newY = (y + el.height()) * scaleY - newHeight; + const newWidth = Math.min( + Math.max((el.width() * scaleX) + 1, 5), + proxyDoc.width() + proxyPadding / 2 - newX + ); + const proxyEl = $('
', { + "class": 'proxy-highlight annotation-hidden', + "css": { + "position": "absolute", + "left": Math.round(newX), + "top": Math.round(newY), + "background-color": color, + "width": newWidth, + "height": newHeight, + } + }).appendTo(proxyDoc); + // Copy data attributes + proxyEl.data(el.data()); + // Set classes for matching + proxyEl.addClass(el.data("match-classes")) + } + ); + } + $('
', { + "class": 'proxy-scroll hidden', + "css": { + "top": proxyScrollTop, + "height": proxyScrollHeight, + } + }).appendTo(proxyDoc); + if (hasScroll()) { + $(".display .proxy-scroll").removeClass("hidden") + } + + $(".display .proxy-doc") + .mousedown(function (event) { + saveDragPos = parseFloat(event.pageY); + isDragging = true; + event.preventDefault(); + }) + .mousemove(function (event) { + const dragPos = parseFloat(event.pageY); + if (isDragging) { + const distanceMoved = dragPos - saveDragPos; + scrollBy(distanceMoved); + saveDragPos = dragPos; + event.preventDefault(); + } + }) + .mouseup(function (event) { + isDragging = false; + }) + .mouseenter(function () { + disableScrollEvent = true; + $(".display .proxy-scroll").addClass("hover") + }) + .mouseleave(function () { + isDragging = false; + disableScrollEvent = false; + $(".display .proxy-scroll").removeClass("hover") + }) + .on('wheel', function (event) { + scrollBy(event.originalEvent.deltaY / 4); + event.preventDefault(); + }); + + // TODO: Handle user clicking in scroll region + + $(".display .main-doc").scroll(function () { + if (disableScrollEvent) return; + $(".display .proxy-scroll") + .css( + "top", $(this).scrollTop() * scaleY + ) + }) + } + + function resizeend() { + if (new Date() - rtime < delta) { + setTimeout(resizeend, delta); + } else { + timeout = false; + updateAnnotations(); + toggleScrollbar(); + } + } + + function toggleScrollbar() { + if (hasScroll()) { + $(".display .proxy-scroll").removeClass("hidden"); + } else { + $(".display .proxy-scroll").addClass("hidden"); + } + } + + function updateAnnotations() { + + annotateSemantic = $("#option-semantic").hasClass("selected"); + annotateLexical = $("#option-lexical").hasClass("selected"); + annotateEntities = $("#option-entity").hasClass("selected"); + annotateNovel = $("#option-novel").hasClass("selected"); + + if (annotateSemantic || annotateLexical) { + $(".summary-item").addClass("selectable") + } else { + $(".summary-item").removeClass("selectable") + } + + if (annotateLexical) { + $(".underline").removeClass("annotation-hidden"); + $(".summary-item").addClass("annotate-lexical"); + } else { + $(".underline").addClass("annotation-hidden"); + $(".summary-item").removeClass("annotate-lexical"); + } + if (annotateSemantic) { + $(".highlight").removeClass("annotation-hidden"); + } else { + $(".highlight").addClass("annotation-hidden"); + } + if (annotateEntities) { + $(".summary-item").addClass("annotate-entities") + } else { + $(".summary-item").removeClass("annotate-entities") + } + if (annotateNovel) { + $(".summary-item").addClass("annotate-novel") + } else { + $(".summary-item").removeClass("annotate-novel") + } + + createProxy(); + + if (annotateLexical) { + $(".proxy-underline").removeClass("annotation-hidden"); + } else { + $(".proxy-underline").addClass("annotation-hidden"); + } + if (annotateSemantic) { + $(".proxy-highlight").removeClass("annotation-hidden"); + } else { + $(".proxy-highlight").addClass("annotation-hidden"); + } + + $(".summary-item .highlight").tooltip("disable"); + if (annotateSemantic) { + $(".summary-item.selected .highlight").tooltip("enable") + } + } + + function removeDocTooltips() { + $("[data-tooltip-timestamp]").tooltip("dispose").removeAttr("data-tooltip-timestamp"); + } + + function resetUnderlines() { + $('.annotation-invisible').removeClass("annotation-invisible"); + $('.annotation-inactive').removeClass("annotation-inactive"); + $('.temp-underline-color') + .each(function () { + $(this).css("border-color", $(this).data("primary-color")); + }) + .removeClass("temp-underline-color") + $('.temp-proxy-underline-color') + .each(function () { + $(this).css("background-color", $(this).data("primary-color")); + }) + .removeClass("temp-proxy-underline-color") + } + + function showDocTooltip(el) { + const topDocHighlightId = $(el).data("top-doc-highlight-id"); + const topDocSim = $(el).data("top-doc-sim"); + const topHighlight = $(`.display .main-doc .highlight[data-highlight-id=${topDocHighlightId}]`); + if (!isViewable(topHighlight)) { + return; + } + topHighlight.tooltip({title: `Most similar (${topDocSim})`, trigger: "manual", container: "body"}); + topHighlight.tooltip("show"); + const tooltipTimestamp = Date.now(); + // Do not use .data() method to set data attributes as they are not searchable + topHighlight.attr("data-tooltip-timestamp", tooltipTimestamp); + setTimeout(function () { + if (topHighlight.data("tooltip-timestamp") == tooltipTimestamp) { + topHighlight.tooltip("dispose").removeAttr("data-tooltip-timestamp"); + } + }, 8000); + } + + function highlightUnderlines() { + const spanId = getSpanId($(this)); + const color = $(this).css("border-bottom-color"); + // TODO Consolidate into single statement + $(`.summary-item.selected .underline.${spanId}`).removeClass("annotation-inactive"); + $(`.doc .underline.${spanId}`) + .removeClass("annotation-inactive") + .each(function () { + $(this).css("border-bottom-color", color); + }) + .addClass("temp-underline-color"); + $(`.proxy-underline.${spanId}`) + .removeClass("annotation-inactive") + .each(function () { + $(this).css("background-color", color); + }) + .addClass("temp-proxy-underline-color"); + + $(`.summary-item.selected .underline:not(.${spanId})`).addClass("annotation-inactive"); + $(`.doc .underline:not(.${spanId})`).addClass("annotation-inactive"); + $(`.proxy-underline:not(.${spanId})`).addClass("annotation-inactive"); + + $(".summary-item.selected .highlight:not(.annotation-hidden)").addClass("annotation-inactive"); + } + + function resetHighlights() { + removeDocTooltips(); + $('.summary-item.selected .annotation-inactive').removeClass("annotation-inactive"); + $('.summary-item.selected .annotation-invisible').removeClass("annotation-invisible"); + $('.temp-highlight-color') + .each(function () { + $(this).css("background-color", $(this).data("primary-color")); + }) + .removeClass("temp-highlight-color"); + $('.highlight.selected').removeClass("selected"); + $('.proxy-highlight.selected').removeClass("selected"); + $('.summary-item [title]').removeAttr("title"); + } + + function highlightToken() { + const highlightId = $(this).data("highlight-id"); + $(`.summary-item.selected .highlight:not(.summary-highlight-${highlightId})`).addClass("annotation-inactive"); + $('.highlight.selected').removeClass("selected") + $('.proxy-highlight.selected').removeClass("selected") + const matchedDocHighlight = `.display .main-doc .summary-highlight-${highlightId}`; + const matchedProxyHighlight = `.proxy-doc .summary-highlight-${highlightId}`; + $(matchedDocHighlight + ", " + matchedProxyHighlight) + .each(function () { + const newHighlightColor = $(this).data(`color-${highlightId}`); + $(this).css("background-color", newHighlightColor); + $(this).addClass("selected"); + }) + .addClass("temp-highlight-color"); + $(".underline").addClass("annotation-inactive"); + $(".proxy-underline").addClass("annotation-invisible") + showDocTooltip(this); + $(this).addClass("selected"); + $(this).removeClass("annotation-inactive"); + $('.summary-item [title]').removeAttr("title"); + if (!isViewable($(matchedDocHighlight))) { + $(this).attr("title", "Click to scroll to most similar word.") + } + } + + function isViewable(el) { + const elTop = el.offset().top; + const elBottom = elTop + el.outerHeight(); + const scrollRegion = $(".display .main-doc"); + const scrollTop = scrollRegion.offset().top; + const scrollBottom = scrollTop + scrollRegion.outerHeight(); + return elTop > scrollTop && elBottom < scrollBottom; + } + + // Initialization + + $(function () { + $('[data-toggle="tooltip"]').tooltip({ + // 'boundary': '.summary-container' + trigger: 'hover' + }) + }) + updateAnnotations(); + + // Bind events + + $(window).resize(function () { + rtime = new Date(); + if (timeout === false) { + timeout = true; + setTimeout(resizeend, delta); + } + }); + + $(".summary-list").on( + "click", + ".summary-item.selectable:not(.selected)", + function () { + const summary_index = $(this).data("index"); + + // Update summary items + $(".summary-item.selected").removeClass("selected") + $(this).addClass("selected") + + // Update doc + // Show the version of document aligned with selected summary index + $(`.doc[data-index=${summary_index}]`).removeClass("nodisplay").addClass("display"); + // Hide the version of document not aligned with selected summary index + $(`.doc[data-index!=${summary_index}]`).removeClass("display").addClass("nodisplay"); + + updateAnnotations(); + } + ); + + $("#option-lexical").click(function () { + $(this).toggleClass("selected") + updateAnnotations() + }); + $("#option-semantic").click(function () { + $(this).toggleClass("selected") + updateAnnotations() + }); + $("#option-novel").click(function () { + $(this).toggleClass("selected") + updateAnnotations() + }); + $("#option-entity").click(function () { + $(this).toggleClass("selected") + updateAnnotations() + }); + + const activeUnderlines = ".summary-item.selected .underline:not(.annotation-inactive):not(.annotation-hidden)"; + $(".summary-list").on( + "mouseenter", + activeUnderlines, + function () { + highlightUnderlines.call(this); + } + ); + + $(".summary-list").on( + "mouseleave", + activeUnderlines, + resetUnderlines + ); + $(".summary-list").on( + "click", + activeUnderlines, + function () { + // Find aligned underline in doc and scroll doc to that position + highlightUnderlines.call(this); + const mainDoc = $(".display .main-doc"); + const spanId = getSpanId($(this)); + const matchedUnderline = $(`.doc .underline.${spanId}`); + mainDoc.animate({ + scrollTop: mainDoc.scrollTop() + + matchedUnderline.offset().top - mainDoc.offset().top - 60 + }, + 300 + ) + } + ); + + const activeHighlights = ".summary-item.selected .highlight:not(.annotation-hidden):not(.matches-ngram), " + + ".summary-item.selected:not(.annotate-lexical) .highlight:not(.annotation-hidden)"; + $(".summary-list").on( + "mouseenter", + activeHighlights, + function () { + highlightToken.call(this); + }) + $(".summary-list").on( + "mouseleave", + activeHighlights, + function () { + resetHighlights(); + resetUnderlines(); + } + ); + $(".summary-list").on( + "click", + activeHighlights, + function () { + highlightToken.call(this); + // Find corresponding highlight in doc representing max similarity and scroll doc to that position + const topDocHighlightId = $(this).data("top-doc-highlight-id"); + removeDocTooltips(topDocHighlightId); + const topDocHighlight = $(`.display .main-doc .highlight[data-highlight-id=${topDocHighlightId}]`); + const mainDoc = $(".display .main-doc"); + const el = this; + mainDoc.animate({ + scrollTop: mainDoc.scrollTop() + + topDocHighlight.offset().top - mainDoc.offset().top - 60 + }, + 300, + function () { + setTimeout( + function () { + // If no other tooltips have since been displayed + if ($("[data-tooltip-timestamp]").length == 0) { + showDocTooltip(el); + } else { + console.log("Not showing tooltip because one already exists") + } + }, + 100 + ) + } + ) + } + ); + $(".summary-list").on( + "mouseleave", + ".summary-item.selected .content", + function () { + resetHighlights(); + resetUnderlines(); + }, + ); + } +); + diff --git a/utils.py b/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..88c90b11e2b38170614ec4332cea2558bea1251e --- /dev/null +++ b/utils.py @@ -0,0 +1,9 @@ +import re + + +def clean_text(text): + split_punct = re.escape(r'()') + return ' '.join(re.findall(rf"[^\s{split_punct}]+|[{split_punct}]", text)) + # Ensure parentheses are probably separated by spaCy tokenizer for CNN/DailyMail dataset. + return text.replace("(", "( ").replace(")", ") ") + diff --git a/website/annotations.png b/website/annotations.png new file mode 100644 index 0000000000000000000000000000000000000000..1400968ec31bd4e86ee4bad381c7d0f4ad77a238 Binary files /dev/null and b/website/annotations.png differ diff --git a/website/demo.gif b/website/demo.gif new file mode 100644 index 0000000000000000000000000000000000000000..e4a076d2180ecbfe8c2a54bf0824d7fbec4894fc --- /dev/null +++ b/website/demo.gif @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:92d7b504318cd2dead51efe53985aa2721f318cc305701dcba80c02a683e78e4 +size 1334322 diff --git a/website/main-vis.jpg b/website/main-vis.jpg new file mode 100644 index 0000000000000000000000000000000000000000..8c8cb1d12b331510d82222786dbdd8f0496df13f Binary files /dev/null and b/website/main-vis.jpg differ diff --git a/website/title.png b/website/title.png new file mode 100644 index 0000000000000000000000000000000000000000..65defd8b6a7ca7e52a00e93043c9028d652dbe25 Binary files /dev/null and b/website/title.png differ diff --git a/website/triangle.png b/website/triangle.png new file mode 100644 index 0000000000000000000000000000000000000000..295e7d4cc79efa5436a6d1e53ad129682f2a4c71 Binary files /dev/null and b/website/triangle.png differ