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        <title>Exploring Content Selection in Summarization of Novel Chapters</title>
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        <namePart type="given">Faisal</namePart>
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    <name type="personal">
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            <namePart type="given">Dan</namePart>
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    <abstract>We present a new summarization task, generating summaries of novel chapters using summary/chapter pairs from online study guides. This is a harder task than the news summarization task, given the chapter length as well as the extreme paraphrasing and generalization found in the summaries. We focus on extractive summarization, which requires the creation of a gold-standard set of extractive summaries. We present a new metric for aligning reference summary sentences with chapter sentences to create gold extracts and also experiment with different alignment methods. Our experiments demonstrate significant improvement over prior alignment approaches for our task as shown through automatic metrics and a crowd-sourced pyramid analysis.</abstract>
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%0 Conference Proceedings
%T Exploring Content Selection in Summarization of Novel Chapters
%A Ladhak, Faisal
%A Li, Bryan
%A Al-Onaizan, Yaser
%A McKeown, Kathleen
%Y Jurafsky, Dan
%Y Chai, Joyce
%Y Schluter, Natalie
%Y Tetreault, Joel
%S Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F ladhak-etal-2020-exploring
%X We present a new summarization task, generating summaries of novel chapters using summary/chapter pairs from online study guides. This is a harder task than the news summarization task, given the chapter length as well as the extreme paraphrasing and generalization found in the summaries. We focus on extractive summarization, which requires the creation of a gold-standard set of extractive summaries. We present a new metric for aligning reference summary sentences with chapter sentences to create gold extracts and also experiment with different alignment methods. Our experiments demonstrate significant improvement over prior alignment approaches for our task as shown through automatic metrics and a crowd-sourced pyramid analysis.
%R 10.18653/v1/2020.acl-main.453
%U https://aclanthology.org/2020.acl-main.453
%U https://doi.org/10.18653/v1/2020.acl-main.453
%P 5043-5054

Markdown (Informal)

[Exploring Content Selection in Summarization of Novel Chapters](https://aclanthology.org/2020.acl-main.453) (Ladhak et al., ACL 2020)

• Exploring Content Selection in Summarization of Novel Chapters (Ladhak et al., ACL 2020)

ACL

• Faisal Ladhak, Bryan Li, Yaser Al-Onaizan, and Kathleen McKeown. 2020. Exploring Content Selection in Summarization of Novel Chapters. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 5043–5054, Online. Association for Computational Linguistics.