billsum / README.md
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metadata
annotations_creators:
  - found
language_creators:
  - found
languages:
  - en
licenses:
  - unknown
multilinguality:
  - monolingual
size_categories:
  - 10K<n<100K
source_datasets:
  - original
task_categories:
  - conditional-text-generation
task_ids:
  - summarization

Dataset Card for "billsum"

Table of Contents

Dataset Description

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

More Information Needed

Languages

More Information Needed

Dataset Structure

We show detailed information for up to 5 configurations of the dataset.

Data Instances

default

  • Size of downloaded dataset files: 64.14 MB
  • Size of the generated dataset: 259.80 MB
  • Total amount of disk used: 323.94 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 Sample Size

name train ca_test test
default 18949 1237 3269

Dataset Creation

Curation Rationale

More Information Needed

Source Data

More Information Needed

Annotations

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


@misc{kornilova2019billsum,
    title={BillSum: A Corpus for Automatic Summarization of US Legislation},
    author={Anastassia Kornilova and Vlad Eidelman},
    year={2019},
    eprint={1910.00523},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}

Contributions

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