metadata
license: cc-by-4.0
task_categories:
- question-answering
- image-to-text
language:
- en
tags:
- chemistry
- biology
- finance
- legal
- medical
- climate
pretty_name: DocGenome
size_categories:
- 1K<n<10K
DocGenome: An Open Large-scale Scientific Document Benchmark for Training and Testing Multi-modal Large Language Models
paper link: DocGenome
We present DocGenome, a structured document dataset constructed by annotating 500K scientific documents from 153 disciplines in the arXiv open-access community, using our custom auto-labeling pipeline DocParser. DocGenome features four characteristics:
- Completeness: It is the first dataset to structure data from all modalities including 13 layout attributes along with their \LaTeX\ source codes.
- Logicality: It provides 6 logical relationships between different entities within each scientific document.
- Diversity: It covers various document-oriented tasks, including document classification, visual grounding, document layout detection, document transformation, open-ended single-page QA and multi-page QA.
- Correctness: It undergoes rigorous quality control checks conducted by a specialized team.
Highlight:
- Data quality rating: for each structured document in DocGenome shown here
- Tutorials on how to use the DocGenome dataset.
- TestSet downloads from Huggingface. If you want to evaluate your model on TestSet, please refer to Evaluation.
DocGenome Benchmark Introduction
Datasets | # Discipline | # Category of Units | # Pages in Train-set | # Pages in Test-set | # Task | # Used Metric | Publication | Entity Relations |
---|---|---|---|---|---|---|---|---|
DocVQA | - | N/A | 11K | 1K | 1 | 2 | 1960-2000 | ❎ |
DocLayNet | - | 11 | 80K | 8K | 1 | 1 | - | ❎ |
DocBank | - | 13 | 0.45M | 50K | 3 | 1 | 2014-2018 | ❎ |
PubLayNet | - | 5 | 0.34M | 12K | 1 | 1 | - | ❎ |
VRDU | - | 10 | 7K | 3K | 3 | 1 | - | ❎ |
DUDE | - | N/A | 20K | 6K | 3 | 3 | 1860-2022 | ❎ |
D^4LA | - | 27 | 8K | 2K | 1 | 3 | - | ❎ |
Fox Benchmark | - | 5 | N/A (No train-set) | 0.2K | 3 | 5 | - | ❎ |
ArXivCap | 32 | N/A | 6.4M* | N/A | 4 | 3 | - | ❎ |
DocGenome (ours) | 153 | 13 | 6.8M | 9K | 7 | 7 | 2007-2022 | ✅ |
Definition of relationships between component units
DocGenome contains 4 level relation types and 2 cite relation types, as shown in the following table:
Name | Description | Example |
---|---|---|
Identical | Two blocks share the same source code. | Cross-column text; Cross-page text. |
Title adjacent | The two titles are adjacent. | (\section{introduction}, \section{method}) |
Subordinate | One block is a subclass of another block. | (\section{introduction}, paragraph within Introduction) |
Non-title adjacent | The two text or equation blocks are adjacent. | (Paragraph 1, Paragraph 2) |
Explicitly-referred | One block refers to another block via footnote, reference, etc. | (As shown in \ref{Fig: 5} ..., Figure 5) |
Implicitly-referred | The caption block refers to the corresponding float environment. | (Table Caption 1, Table 1) |
Attribute of component units
DocGenome has 13 attributes of component units, which can be categorized into two classes
- 1) Fixed-form units, including Text, Title, Abstract, etc., which are characterized by sequential reading and hierarchical relationships readily discernible from the list obtained in Stage-two of the designed DocParser.
- 2) Floating-form units, including Table, Figure, etc., which establish directional references to fixed-form units through commands like \texttt{\textbackslash ref} and \texttt{\textbackslash label}.
Index | Category | Notes |
---|---|---|
0 | Algorithm | |
1 | Caption | Titles of Images, Tables, and Algorithms |
2 | Equation | |
3 | Figure | |
4 | Footnote | |
5 | List | |
7 | Table | |
8 | Text | |
9 | Text-EQ | Text block with inline equations |
10 | Title | Section titles |
12 | PaperTitle | |
13 | Code | |
14 | Abstract |
Citation
If you find our work useful in your research, please consider citing Fox:
@article{xia2024docgenome,
title={DocGenome: An Open Large-scale Scientific Document Benchmark for Training and Testing Multi-modal Large Language Models},
author={Xia, Renqiu and Mao, Song and Yan, Xiangchao and Zhou, Hongbin and Zhang, Bo and Peng, Haoyang and Pi, Jiahao and Fu, Daocheng and Wu, Wenjie and Ye, Hancheng and others},
journal={arXiv preprint arXiv:2406.11633},
year={2024}
}