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        <title>Towards Improving Abstractive Summarization via Entailment Generation</title>
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    <name type="personal">
        <namePart type="given">Ramakanth</namePart>
        <namePart type="family">Pasunuru</namePart>
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        <namePart type="given">Han</namePart>
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    <name type="personal">
        <namePart type="given">Mohit</namePart>
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            <title>Proceedings of the Workshop on New Frontiers in Summarization</title>
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            <namePart type="given">Lu</namePart>
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            <namePart type="given">Jackie</namePart>
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            <namePart type="given">Fei</namePart>
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    <abstract>Abstractive summarization, the task of rewriting and compressing a document into a short summary, has achieved considerable success with neural sequence-to-sequence models. However, these models can still benefit from stronger natural language inference skills, since a correct summary is logically entailed by the input document, i.e., it should not contain any contradictory or unrelated information. We incorporate such knowledge into an abstractive summarization model via multi-task learning, where we share its decoder parameters with those of an entailment generation model. We achieve promising initial improvements based on multiple metrics and datasets (including a test-only setting). The domain mismatch between the entailment (captions) and summarization (news) datasets suggests that the model is learning some domain-agnostic inference skills.</abstract>
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    <identifier type="doi">10.18653/v1/W17-4504</identifier>
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        <date>2017-09</date>
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%0 Conference Proceedings
%T Towards Improving Abstractive Summarization via Entailment Generation
%A Pasunuru, Ramakanth
%A Guo, Han
%A Bansal, Mohit
%Y Wang, Lu
%Y Cheung, Jackie Chi Kit
%Y Carenini, Giuseppe
%Y Liu, Fei
%S Proceedings of the Workshop on New Frontiers in Summarization
%D 2017
%8 September
%I Association for Computational Linguistics
%C Copenhagen, Denmark
%F pasunuru-etal-2017-towards
%X Abstractive summarization, the task of rewriting and compressing a document into a short summary, has achieved considerable success with neural sequence-to-sequence models. However, these models can still benefit from stronger natural language inference skills, since a correct summary is logically entailed by the input document, i.e., it should not contain any contradictory or unrelated information. We incorporate such knowledge into an abstractive summarization model via multi-task learning, where we share its decoder parameters with those of an entailment generation model. We achieve promising initial improvements based on multiple metrics and datasets (including a test-only setting). The domain mismatch between the entailment (captions) and summarization (news) datasets suggests that the model is learning some domain-agnostic inference skills.
%R 10.18653/v1/W17-4504
%U https://aclanthology.org/W17-4504/
%U https://doi.org/10.18653/v1/W17-4504
%P 27-32

Markdown (Informal)

[Towards Improving Abstractive Summarization via Entailment Generation](https://aclanthology.org/W17-4504/) (Pasunuru et al., 2017)

• Towards Improving Abstractive Summarization via Entailment Generation (Pasunuru et al., 2017)

ACL

• Ramakanth Pasunuru, Han Guo, and Mohit Bansal. 2017. Towards Improving Abstractive Summarization via Entailment Generation. In Proceedings of the Workshop on New Frontiers in Summarization, pages 27–32, Copenhagen, Denmark. Association for Computational Linguistics.