  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.
