Update README.md

README.md

@@ -1,7 +1,3 @@
-Citation:
-
-
----
 annotations_creators:
 - found
 language:
@@ -30,38 +26,24 @@ task_categories:
 - other
 task_ids:
 - multi-class-classification
-- multi-label-classification---
+- multi-label-classification
 
-# Dataset Card for [Dataset Name]
+# Dataset Card for [AuthorMix]
 
 ## Table of Contents
 - [Table of Contents](#table-of-contents)
 - [Dataset Description](#dataset-description)
-
   - [Dataset Summary](#dataset-summary)
-
   - [Languages](#languages)
-    English
 - [Dataset Structure](#dataset-structure)
-
   - [Data Instances](#data-instances)
-  - [Data Fields](#data-fields)
   - [Data Splits](#data-splits)
 - [Dataset Creation](#dataset-creation)
   - [Curation Rationale](#curation-rationale)
   - [Source Data](#source-data)
-  - [Annotations](#annotations)
-  - [Personal and Sensitive Information](#personal-and-sensitive-information)
-- [Considerations for Using the Data](#considerations-for-using-the-data)
-  - [Social Impact of Dataset](#social-impact-of-dataset)
-  - [Discussion of Biases](#discussion-of-biases)
-  - [Other Known Limitations](#other-known-limitations)
 - [Additional Information](#additional-information)
-  - [Dataset Curators](#dataset-curators)
-  - [Licensing Information](#licensing-information)
   - [Citation Information](#citation-information)
   - [Contributions](#contributions)
-
 ## Dataset Description
 AUTHORMIX, was originally created for authorship obfuscation task and had data from four distinct domains: presidential speeches, early-1900s fiction novels, scholarly articles, and diary-style blogs. Altogether, AUTHORMIX contains over 30k high-quality paragraphs from 14 authors.
 
@@ -94,12 +76,12 @@ No preset data splits available.
 ## Dataset Creation
 For the presidential domain, we curate and clean a novel collection of high-quality presidential speeches from George W. Bush (n=38), Barack Obama (n=29), and Donald Trump (n=26), transcribed by  the Miller Center (https://data.millercenter.org) at the University of Virginia. We broke the speeches naturally into paragraphs and then selected all paragraphs between 2-5 sentences. This resulted in a total of n= 13K paragraphs. 
 
-Similarly, we also decided to develop a new collection of early 1900s fiction writers from the with strong writing styles, therefore we choose text from books by Ernest Hemingway, F. Scott Fitzgerald, and Virginia Woolf which were collected from Project Gutenbert (https://www.gutenberg.org). We selected the top 4 most popular books on Project Gutenberg for each author and then again, used the natural paragraphs from each author. We selected all paragraphs between 2-5 sentences. This resulted in a total of n= 9K paragraphs. 
+Similarly, we also decided to develop a new collection of early 1900s fiction writers from the with strong writing styles, therefore we choose text from books by Ernest Hemingway, F. Scott Fitzgerald, and Virginia Woolf which were collected from Project Gutenberg (https://www.gutenberg.org). We selected the top 4 most popular books on Project Gutenberg for each author and then again, used the natural paragraphs from each author. We selected all paragraphs between 2-5 sentences. This resulted in a total of n= 9K paragraphs. 
 
 Lastly, we altered the existing data from two current datasets, the Extended-Brennan Greenstad which is a collection of "scholarly" short (500-word) paragraphs gathered from Amazon Mechanical Turk (AMT) and the Blog Authorship corpus, a collection of blogs (diary-style entries) that were posted to blog.com. For the AMT dataset, we used authors "h", "pp", and "qq" and we artificially created paragraphs by chunking the text into a random collection of 2-5 sentences (as the text is not naturally broken into paragraphs). For the Blog dataset, we used authors "5546", "11518", "25872", "30102", "30407", we used the natural paragraphs. Then, to match the speech and novel domains, we edited to include all paragraphs between 2-5 sentences and at least 3 words. This resulted in n= 500 and n= 8K paragraphs for the AMT and Blog accordingly. 
 ### Curation Rationale
 
-This dataset was creatd specifically for authorship obfuscation. We wanted to create a diverse high-quality dataset, which had domains of writings with similar topics but strong stylstic writing. 
+This dataset was created specifically for authorship obfuscation. We wanted to create a diverse high-quality dataset, which had domains of writings with similar topics but strong stylstic writing. 
 
 ### Source Data
 Miller Center (https://data.millercenter.org)