On Generating Extended Summaries of Long Documents
By Sajad Sotudeh, Arman Cohan, Nazli Goharian. 2020
19 citations recorded by the source corpus.
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 this https URL
Subfields: Artificial Intelligence, Natural Language Processing
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In Artificial Intelligence
- Jan Leike
- Jeff Wu
- Ryan J. Lowe
- Daniel M. Ziegler
- Paul Christiano
- Long Ouyang
- Nissan Stiennon
- Sergey Levine
In Natural Language Processing
- Paul Christiano
- Daniel M. Ziegler
- Ryan J. Lowe
- Nissan Stiennon
- Jan Leike
- Long Ouyang
- Jeff Wu
- Samuel R. Bowman
Citations in the corpus
- Recursively Summarizing Books with Human Feedback (2021, background): “…papers (Abu-Jbara and Radev, 2011; Collins et al., 2017; Subramanian et al., 2019; Cohan et al., 2018; Xiao and Carenini, 2019; Zhao et al., 2020; Sotudeh et al., 2020), and patents (Sharma et al., 2019), as well as multi-document summarization (Liu et al., 2018; Ma et al., 2020; Gharebagh et…”