ubaada/booksum-complete-cleaned

Table of Contents

1. 1.Description
2. 2.Usage
3. 3.Distribution
4. 4.Chapters Dataset
5. 5.Books Dataset
6. 6.Structure
7. 7.Results and Comparison with kmfoda/booksum

Description:

This repository contains the Booksum dataset introduced in the paper BookSum: A Collection of Datasets for Long-form Narrative Summarization .

This dataset includes both book and chapter summaries from the BookSum dataset (unlike the kmfoda/booksum one which only contains the chapter dataset). Some mismatched summaries have been corrected. Uneccessary columns have been discarded. Contains minimal text-to-summary rows. As there are multiple summaries for a given text, each row contains an array of summaries.

Usage

Note: Make sure you have >2.14.0 version of "datasets" library installed to load the dataset successfully.

from datasets import load_dataset

book_data = load_dataset("ubaada/booksum-complete-cleaned", "books")
chapter_data = load_dataset("ubaada/booksum-complete-cleaned", "chapters")


# Print the 1st book
print(book_data["train"][0]['text'])

# Print the summary of the 1st book 
print(book_data["train"][0]['summary'][0]['text'])

Distribution

<div style="display: inline-block; vertical-align: top; width: 45%;">

Chapters Dataset

</div>

<div style="display: inline-block; vertical-align: top; width: 45%; margin-left: 5%;">

Books Dataset

</div>

Structure:

Chapters Dataset
  0 - bid (book id) 
  1 - book_title
  2 - chapter_id
  3 - text (raw chapter text)
  4 - summary (list of summaries from different sources)
      - {source, text (summary), analysis}
      ...
  5 - is_aggregate (bool) (if true, then the text contains more than one chapter)

Books Dataset:
  0 - bid (book id)
  1 - title
  2 - text (raw text)
  4 - summary (list of summaries from different sources)
      - {source, text (summary), analysis}
      ...

Reults and Comparison with kmfoda/booksum

Tested on the 'test' split of chapter sub-dataset. There are slight improvement on R1/R2 scores compared to another BookSum repo likely due to the work done on cleaning the misalignments in the alignment file. In the plot for this dataset, first summary \[0\] is chosen for each chapter. If best reference summary is chosen from the list for each chapter, theere are further improvements but are not shown here for fairness.