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        <title>Inducing Document Structure for Aspect-based Summarization</title>
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            <namePart type="family">Korhonen</namePart>
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    <abstract>Automatic summarization is typically treated as a 1-to-1 mapping from document to summary. Documents such as news articles, however, are structured and often cover multiple topics or aspects; and readers may be interested in only some of them. We tackle the task of aspect-based summarization, where, given a document and a target aspect, our models generate a summary centered around the aspect. We induce latent document structure jointly with an abstractive summarization objective, and train our models in a scalable synthetic setup. In addition to improvements in summarization over topic-agnostic baselines, we demonstrate the benefit of the learnt document structure: we show that our models (a) learn to accurately segment documents by aspect; (b) can leverage the structure to produce both abstractive and extractive aspect-based summaries; and (c) that structure is particularly advantageous for summarizing long documents. All results transfer from synthetic training documents to natural news articles from CNN/Daily Mail and RCV1.</abstract>
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%0 Conference Proceedings
%T Inducing Document Structure for Aspect-based Summarization
%A Frermann, Lea
%A Klementiev, Alexandre
%Y Korhonen, Anna
%Y Traum, David
%Y Màrquez, Lluís
%S Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
%D 2019
%8 July
%I Association for Computational Linguistics
%C Florence, Italy
%F frermann-klementiev-2019-inducing
%X Automatic summarization is typically treated as a 1-to-1 mapping from document to summary. Documents such as news articles, however, are structured and often cover multiple topics or aspects; and readers may be interested in only some of them. We tackle the task of aspect-based summarization, where, given a document and a target aspect, our models generate a summary centered around the aspect. We induce latent document structure jointly with an abstractive summarization objective, and train our models in a scalable synthetic setup. In addition to improvements in summarization over topic-agnostic baselines, we demonstrate the benefit of the learnt document structure: we show that our models (a) learn to accurately segment documents by aspect; (b) can leverage the structure to produce both abstractive and extractive aspect-based summaries; and (c) that structure is particularly advantageous for summarizing long documents. All results transfer from synthetic training documents to natural news articles from CNN/Daily Mail and RCV1.
%R 10.18653/v1/P19-1630
%U https://aclanthology.org/P19-1630
%U https://doi.org/10.18653/v1/P19-1630
%P 6263-6273

Markdown (Informal)

[Inducing Document Structure for Aspect-based Summarization](https://aclanthology.org/P19-1630) (Frermann & Klementiev, ACL 2019)

• Inducing Document Structure for Aspect-based Summarization (Frermann & Klementiev, ACL 2019)

ACL

• Lea Frermann and Alexandre Klementiev. 2019. Inducing Document Structure for Aspect-based Summarization. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 6263–6273, Florence, Italy. Association for Computational Linguistics.