  Prior work in document summarization has mainly focused on generating short
summaries of a document. While this type of summary helps get a high-level view
of a given document, it is desirable in some cases to know more detailed
information about its salient points that can't fit in a short summary. This is
typically the case for longer documents such as a research paper, legal
document, or a book. In this paper, we present a new method for generating
extended summaries of long papers. Our method exploits hierarchical structure
of the documents and incorporates it into an extractive summarization model
through a multi-task learning approach. We then present our results on three
long summarization datasets, arXiv-Long, PubMed-Long, and Longsumm. Our method
outperforms or matches the performance of strong baselines. Furthermore, we
perform a comprehensive analysis over the generated results, shedding insights
on future research for long-form summary generation task. Our analysis shows
that our multi-tasking approach can adjust extraction probability distribution
to the favor of summary-worthy sentences across diverse sections. Our datasets,
and codes are publicly available at
https://github.com/Georgetown-IR-Lab/ExtendedSumm

Computation and Language