BillSum: A Corpus for Automatic Summarization of US Legislation

Anastassia Kornilova, Vlad Eidelman

2019-10-01WS 2019 11Text Summarization

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 (https://github.com/FiscalNote/BillSum). 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 bills, thus, showing that methods developed on this dataset can transfer to states without human-written summaries.

Results

Task	Dataset	Metric	Value	Model
Text Summarization	BillSum	rouge1	38.65	Longformer Encoder Decoder

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