An Examination of BillSum: An Automatic Summarization Corpus for U.S. Legislation
The increasing volume of legislative documents produced annually by the U.S. federal and state governments presents a significant challenge for individuals who need to quickly access salient information from long and technical legislative texts. The paper "BillSum: A Corpus for Automatic Summarization of US Legislation" by Kornilova and Eidelman addresses this issue by introducing BillSum, a specialized dataset aimed at improving the efficacy of automatic summarization models in the legal domain. Through this work, the authors extend the application scope of summarization from traditional domains such as news and scientific articles to the nuanced field of legislative documents.
The BillSum dataset comprises 22,218 U.S. Congressional bills and summaries, along with an additional set of 1,237 California bills. These documents are collected from the Govinfo service, which provides text from the United States Government Publishing Office. The dataset focuses on mid-length legislation, specifically those ranging from 5,000 to 20,000 characters, which are detailed enough to warrant summaries but not excessively lengthy to complicate the summarization task. Notably, the BillSum dataset includes measures to eliminate redundancy by removing bills with high cosine similarity in text.
Benchmarking extractive summarization methods, the authors evaluate several models, including Document Context (DOC) models that consider sentence importance through position and TF-IDF features, as well as Summary Language (SUM) models utilizing neural architectures such as BERT. The ensemble of these approaches exhibits superior performance on the dataset compared to baseline extractive models like SumBasic and TextRank, but remains significantly behind an oracle model relying on true sentence relevance.
One of the significant claims of the paper is the transferability of summarization models trained on the U.S. Congressional bills to California bills, for which human-written summaries are not typically provided. This cross-domain application underscores the potential utility of BillSum in aiding state legislatures, which lack resources for creating human-generated summaries, to adopt automatic summarization tools effectively.
Experimentally, the ensemble DOC+SUM model achieved the highest performance with a Rouge-2 F-Score, improving upon both standalone models DOC and SUM, although there remains a large performance gap compared with the oracle benchmark. The results demonstrate that while the extractive models can identify summary-worthy content, the task of summarizing legislative text — particularly capturing its full context and semantics — remains complex and requires further model development.
The findings suggest several avenues for future research and development in the AI summarization field. Firstly, integrating additional linguistic and structural features into neural models may enhance model capabilities. Secondly, exploring finer-grained extractive and abstractive strategies might improve the precision of generated summaries. Thirdly, the expansion of BillSum to include more states or international legislative texts could broaden the applicability of summarization models across legal systems.
In summary, Kornilova and Eidelman establish a pioneering effort to develop a corpus specifically tailored for legislative summarization. This work serves as a critical step toward enabling legal scholars, policymakers, and citizens to more effectively interact with legislative data through automated methods, highlighting the importance of nuanced domain adaptation in natural language processing tasks.