Update README.md

README.md

@@ -14,7 +14,6 @@ language:
 - **Homepage:** https://github.com/roemmele/AbLit
 - **Repository:** https://github.com/roemmele/AbLit
 - **Paper:** https://arxiv.org/pdf/2302.06579.pdf
-- **Leaderboard:** 
 - **Point of Contact:** Melissa Roemmele (melissa@roemmele.io)
 
 ### Dataset Summary
@@ -23,34 +22,28 @@ The AbLit dataset contains **ab**ridged versions of 10 classic English **lit**er
 The abridgements were written and made publically available by Emma Laybourn [here](http://www.englishliteratureebooks.com/classicnovelsabridged.html).
 This is the first known dataset for NLP research that focuses on the abridgement task. See the paper for details.
 
-### Supported Tasks and Leaderboards
-
-None
-
 ### Languages
 
 English
 
 ## Dataset Structure
 
-Each passage in the original version of a book chapter is aligned with its corresponding passage in the abridged version. These aligned pairs are available for various passage sizes: sentences, paragraphs, and multi-paragraph "chunks". The passage size is specified when loading the dataset. There are train/dev/test splits for items of each size.  
+Each passage in the original version of a book chapter is aligned with its corresponding passage in the abridged version. These aligned pairs are available for various passage sizes: sentences, paragraphs, and multi-paragraph "chunks". The passage size is specified when loading the dataset. There are train/dev/test splits for items of each size.
 
-### Data Instances
+| Passage Size  | Description | # Train | # Dev | # Test |
+| ------------- | ------------- | ------- | ------- | ------- |
+| Chapters | Each passage is a single chapter | 808 | 10 | 50
+| Sentences  | Each passage is a sentence delimited by the NLTK sentence tokenizer | 122,219 | 1,143 | 10,431 |
+| Paragraphs | Each passage is a paragraph delimited by a line break | 37,227 | 313 | 3,125 |
+| Chunks | Each passage consists of up to X number of sentences, which may span more than one paragraph. X=10 is provided here; to derive chunks with other lengths X, see GitHub repo) | 14,857 | 141 | 1,264
 
-Original-abridged chapter pairs:
-  - Train: 808
-  - Dev: 10
-  - Test: 50
+#### Example Usage
 
-Original-abridged paragraph pairs:
-  - Train: 37,227
-  - Dev: 313
-  - Test: 3,125
-
-Original-abridged sentence pairs:
-  - Train: 122,219
-  - Dev: 1,143
-  - Test: 10,431
+To load aligned sentences:
+``
+from datasets import load_dataset
+data = load_dataset("ablit", "sentences")
+``
 
 ### Data Fields
 
@@ -59,12 +52,6 @@ Abridged: passage text in the abridged version
 Book: title of book containing passage
 Chapter: title of chapter containing passage
 
-### Data Splits
-
-Train: ~99% alignment accuracy (see paper)
-Dev: 100% alignment accuracy
-Test: 100% alignment accuracy
-
 ## Dataset Creation
 
 ### Curation Rationale
@@ -77,7 +64,7 @@ The author Emma Laybourn wrote abridged versions of classic English literature b
 
 #### Initial Data Collection and Normalization
 
-We downloaded the original and abridged versions of the books from the respective websites.
+We obtained the original and abridged versions of the books from the respective websites.
 
 #### Who are the source language producers?
 
@@ -87,7 +74,7 @@ Emma Laybourn
 
 #### Annotation process
 
-We designed a procedure for automatically aligning passages between the original and abridged version of each chapter. We conducted a human evaluation to verify these alignments had high accuracy. The dev and test splits of the dataset were fully validated to ensure 100% alignment accuracy.
+We designed a procedure for automatically aligning passages between the original and abridged version of each chapter. We conducted a human evaluation to verify these alignments had high accuracy. The training split of the dataset has ~99% accuracy. The dev and test splits of the dataset were fully human-validated to ensure 100% accuracy. See the paper for further explanation.
 
 #### Who are the annotators?