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        <title>Unsupervised Aspect-Based Multi-Document Abstractive Summarization</title>
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    <name type="personal">
        <namePart type="given">Maximin</namePart>
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    <name type="personal">
        <namePart type="given">Matthias</namePart>
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            <title>Proceedings of the 2nd Workshop on New Frontiers in Summarization</title>
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        <name type="personal">
            <namePart type="given">Lu</namePart>
            <namePart type="family">Wang</namePart>
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        <name type="personal">
            <namePart type="given">Jackie</namePart>
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            <namePart type="given">Kit</namePart>
            <namePart type="family">Cheung</namePart>
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            <namePart type="given">Giuseppe</namePart>
            <namePart type="family">Carenini</namePart>
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        <name type="personal">
            <namePart type="given">Fei</namePart>
            <namePart type="family">Liu</namePart>
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    <abstract>User-generated reviews of products or services provide valuable information to customers. However, it is often impossible to read each of the potentially thousands of reviews: it would therefore save valuable time to provide short summaries of their contents. We address opinion summarization, a multi-document summarization task, with an unsupervised abstractive summarization neural system. Our system is based on (i) a language model that is meant to encode reviews to a vector space, and to generate fluent sentences from the same vector space (ii) a clustering step that groups together reviews about the same aspects and allows the system to generate summary sentences focused on these aspects. Our experiments on the Oposum dataset empirically show the importance of the clustering step.</abstract>
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    <identifier type="doi">10.18653/v1/D19-5405</identifier>
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        <date>2019-11</date>
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%0 Conference Proceedings
%T Unsupervised Aspect-Based Multi-Document Abstractive Summarization
%A Coavoux, Maximin
%A Elsahar, Hady
%A Gallé, Matthias
%Y Wang, Lu
%Y Cheung, Jackie Chi Kit
%Y Carenini, Giuseppe
%Y Liu, Fei
%S Proceedings of the 2nd Workshop on New Frontiers in Summarization
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F coavoux-etal-2019-unsupervised
%X User-generated reviews of products or services provide valuable information to customers. However, it is often impossible to read each of the potentially thousands of reviews: it would therefore save valuable time to provide short summaries of their contents. We address opinion summarization, a multi-document summarization task, with an unsupervised abstractive summarization neural system. Our system is based on (i) a language model that is meant to encode reviews to a vector space, and to generate fluent sentences from the same vector space (ii) a clustering step that groups together reviews about the same aspects and allows the system to generate summary sentences focused on these aspects. Our experiments on the Oposum dataset empirically show the importance of the clustering step.
%R 10.18653/v1/D19-5405
%U https://aclanthology.org/D19-5405
%U https://doi.org/10.18653/v1/D19-5405
%P 42-47

Markdown (Informal)

[Unsupervised Aspect-Based Multi-Document Abstractive Summarization](https://aclanthology.org/D19-5405) (Coavoux et al., 2019)

• Unsupervised Aspect-Based Multi-Document Abstractive Summarization (Coavoux et al., 2019)

ACL

• Maximin Coavoux, Hady Elsahar, and Matthias Gallé. 2019. Unsupervised Aspect-Based Multi-Document Abstractive Summarization. In Proceedings of the 2nd Workshop on New Frontiers in Summarization, pages 42–47, Hong Kong, China. Association for Computational Linguistics.