BillSum: A Corpus for Automatic Summarization of US Legislation

Anastassia Kornilova, Vladimir Eidelman


Abstract
Automatic summarization methods have been studied on a variety of domains, including news and scientific articles. Yet, legislation has not previously been considered for this task, despite US Congress and state governments releasing tens of thousands of bills every year. In this paper, we introduce BillSum, the first dataset for summarization of US Congressional and California state bills. We explain the properties of the dataset that make it more challenging to process than other domains. Then, we benchmark extractive methods that consider neural sentence representations and traditional contextual features. Finally, we demonstrate that models built on Congressional bills can be used to summarize California billa, thus, showing that methods developed on this dataset can transfer to states without human-written summaries.
Anthology ID:
D19-5406
Original:
D19-5406v1
Version 2:
D19-5406v2
Volume:
Proceedings of the 2nd Workshop on New Frontiers in Summarization
Month:
November
Year:
2019
Address:
Hong Kong, China
Venues:
EMNLP | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
48–56
Language:
URL:
https://aclanthology.org/D19-5406
DOI:
10.18653/v1/D19-5406
Bibkey:
Cite (ACL):
Anastassia Kornilova and Vladimir Eidelman. 2019. BillSum: A Corpus for Automatic Summarization of US Legislation. In Proceedings of the 2nd Workshop on New Frontiers in Summarization, pages 48–56, Hong Kong, China. Association for Computational Linguistics.
Cite (Informal):
BillSum: A Corpus for Automatic Summarization of US Legislation (Kornilova & Eidelman, EMNLP 2019)
Copy Citation:
PDF:
https://aclanthology.org/D19-5406.pdf
Code
 FiscalNote/BillSum +  additional community code
Data
BillSum