  Most existing text summarization datasets are compiled from the news domain,
where summaries have a flattened discourse structure. In such datasets,
summary-worthy content often appears in the beginning of input articles.
Moreover, large segments from input articles are present verbatim in their
respective summaries. These issues impede the learning and evaluation of
systems that can understand an article's global content structure as well as
produce abstractive summaries with high compression ratio. In this work, we
present a novel dataset, BIGPATENT, consisting of 1.3 million records of U.S.
patent documents along with human written abstractive summaries. Compared to
existing summarization datasets, BIGPATENT has the following properties: i)
summaries contain a richer discourse structure with more recurring entities,
ii) salient content is evenly distributed in the input, and iii) lesser and
shorter extractive fragments are present in the summaries. Finally, we train
and evaluate baselines and popular learning models on BIGPATENT to shed light
on new challenges and motivate future directions for summarization research.
