README.md

metadata

annotations_creators:
  - found
language_creators:
  - found
language:
  - en
license:
  - cc0-1.0
multilinguality:
  - monolingual
size_categories:
  - 10K<n<100K
source_datasets:
  - original
task_categories:
  - summarization
task_ids: []
paperswithcode_id: billsum
pretty_name: BillSum
tags:
  - bills-summarization
dataset_info:
  features:
    - name: text
      dtype: string
    - name: summary
      dtype: string
    - name: title
      dtype: string
  splits:
    - name: train
      num_bytes: 219596090
      num_examples: 18949
    - name: test
      num_bytes: 37866257
      num_examples: 3269
    - name: ca_test
      num_bytes: 14945291
      num_examples: 1237
  download_size: 113729382
  dataset_size: 272407638
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
      - split: test
        path: data/test-*
      - split: ca_test
        path: data/ca_test-*
train-eval-index:
  - config: default
    task: summarization
    task_id: summarization
    splits:
      train_split: train
      eval_split: test
    col_mapping:
      text: text
      summary: target
    metrics:
      - type: rouge
        name: Rouge

Dataset Card for "billsum"

Table of Contents

• Dataset Description
  • Dataset Summary
  • Supported Tasks and Leaderboards
  • Languages
• Dataset Structure
  • Data Instances
  • Data Fields
  • Data Splits
• Dataset Creation
  • Curation Rationale
  • Source Data
  • Annotations
  • Personal and Sensitive Information
• Considerations for Using the Data
  • Social Impact of Dataset
  • Discussion of Biases
  • Other Known Limitations
• Additional Information
  • Dataset Curators
  • Licensing Information
  • Citation Information
  • Contributions

Dataset Description

• Homepage: https://github.com/FiscalNote/BillSum
• Repository: More Information Needed
• Paper: https://aclanthology.org/D19-5406/
• Paper: https://arxiv.org/abs/1910.00523
• Point of Contact: More Information Needed
• Size of downloaded dataset files: 67.26 MB
• Size of the generated dataset: 272.42 MB
• Total amount of disk used: 339.68 MB

Dataset Summary

BillSum, summarization of US Congressional and California state bills.

There are several features:

• text: bill text.
• summary: summary of the bills.
• title: title of the bills. features for us bills. ca bills does not have.
• text_len: number of chars in text.
• sum_len: number of chars in summary.

Supported Tasks and Leaderboards

More Information Needed

Languages

More Information Needed

Dataset Structure

Data Instances

default

• Size of downloaded dataset files: 67.26 MB
• Size of the generated dataset: 272.42 MB
• Total amount of disk used: 339.68 MB

An example of 'train' looks as follows.

{
    "summary": "some summary",
    "text": "some text.",
    "title": "An act to amend Section xxx."
}

Data Fields

The data fields are the same among all splits.

default

• text: a string feature.
• summary: a string feature.
• title: a string feature.

Data Splits

name	train	ca_test	test
default	18949	1237	3269

Dataset Creation

Curation Rationale

More Information Needed

Source Data

Initial Data Collection and Normalization

The data consists of three parts: US training bills, US test bills and California test bills. The US bills were collected from the Govinfo service provided by the United States Government Publishing Office (GPO) under CC0-1.0 license. The California, bills from the 2015-2016 session are available from the legislature’s website.

Who are the source language producers?

More Information Needed

Annotations

Annotation process

More Information Needed

Who are the annotators?

More Information Needed

Personal and Sensitive Information

More Information Needed

Considerations for Using the Data

Social Impact of Dataset

More Information Needed

Discussion of Biases

More Information Needed

Other Known Limitations

More Information Needed

Additional Information

Dataset Curators

More Information Needed

Licensing Information

More Information Needed

Citation Information

@inproceedings{kornilova-eidelman-2019-billsum,
    title = "{B}ill{S}um: A Corpus for Automatic Summarization of {US} Legislation",
    author = "Kornilova, Anastassia  and
      Eidelman, Vladimir",
    editor = "Wang, Lu  and
      Cheung, Jackie Chi Kit  and
      Carenini, Giuseppe  and
      Liu, Fei",
    booktitle = "Proceedings of the 2nd Workshop on New Frontiers in Summarization",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/D19-5406",
    doi = "10.18653/v1/D19-5406",
    pages = "48--56",
    eprint={1910.00523},
    archivePrefix={arXiv},
    primaryClass={cs.CL},
}

Contributions

Thanks to @thomwolf, @jplu, @lewtun for adding this dataset.