BillSum：一个US立法自动摘要语料库
BillSum: A Corpus for Automatic Summarization of US Legislation
摘要：
自动摘要方法已经在包括新闻和科学文章在内的各种领域进行了研究。然而，尽管美国国会和州政府每年发布数万项法案，但此前从未考虑过这项任务的立法。在本文中，我们介绍了BillSum，这是第一个用于总结美国国会和加利福尼亚州法案的数据集（https：//github.com/FiscalNote/BillSum）。我们解释了数据集的属性，使其比其他领域更具挑战性。然后，我们对考虑神经句子表示和传统上下文特征的提取方法进行基准测试。最后，我们证明了建立在国会法案上的模型可以用来总结加利福尼亚州的法案，从而表明在这个数据集上开发的方法可以转移到没有人类书面摘要的州。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
