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        <title>Not All Reviews Are Equal: Towards Addressing Reviewer Biases for Opinion Summarization</title>
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        <namePart type="given">Wenyi</namePart>
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            <title>Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop</title>
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        <name type="personal">
            <namePart type="given">Fernando</namePart>
            <namePart type="family">Alva-Manchego</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Eunsol</namePart>
            <namePart type="family">Choi</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Daniel</namePart>
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    <abstract>Consumers read online reviews for insights which help them to make decisions. Given the large volumes of reviews, succinct review summaries are important for many applications. Existing research has focused on mining for opinions from only review texts and largely ignores the reviewers. However, reviewers have biases and may write lenient or harsh reviews; they may also have preferences towards some topics over others. Therefore, not all reviews are equal. Ignoring the biases in reviews can generate misleading summaries. We aim for summarization of reviews to include balanced opinions from reviewers of different biases and preferences. We propose to model reviewer biases from their review texts and rating distributions, and learn a bias-aware opinion representation. We further devise an approach for balanced opinion summarization of reviews using our bias-aware opinion representation.</abstract>
    <identifier type="citekey">tay-2019-reviews</identifier>
    <identifier type="doi">10.18653/v1/P19-2005</identifier>
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        <date>2019-07</date>
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            <start>34</start>
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%0 Conference Proceedings
%T Not All Reviews Are Equal: Towards Addressing Reviewer Biases for Opinion Summarization
%A Tay, Wenyi
%Y Alva-Manchego, Fernando
%Y Choi, Eunsol
%Y Khashabi, Daniel
%S Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop
%D 2019
%8 July
%I Association for Computational Linguistics
%C Florence, Italy
%F tay-2019-reviews
%X Consumers read online reviews for insights which help them to make decisions. Given the large volumes of reviews, succinct review summaries are important for many applications. Existing research has focused on mining for opinions from only review texts and largely ignores the reviewers. However, reviewers have biases and may write lenient or harsh reviews; they may also have preferences towards some topics over others. Therefore, not all reviews are equal. Ignoring the biases in reviews can generate misleading summaries. We aim for summarization of reviews to include balanced opinions from reviewers of different biases and preferences. We propose to model reviewer biases from their review texts and rating distributions, and learn a bias-aware opinion representation. We further devise an approach for balanced opinion summarization of reviews using our bias-aware opinion representation.
%R 10.18653/v1/P19-2005
%U https://aclanthology.org/P19-2005
%U https://doi.org/10.18653/v1/P19-2005
%P 34-42

Markdown (Informal)

[Not All Reviews Are Equal: Towards Addressing Reviewer Biases for Opinion Summarization](https://aclanthology.org/P19-2005) (Tay, ACL 2019)

• Not All Reviews Are Equal: Towards Addressing Reviewer Biases for Opinion Summarization (Tay, ACL 2019)

ACL

• Wenyi Tay. 2019. Not All Reviews Are Equal: Towards Addressing Reviewer Biases for Opinion Summarization. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop, pages 34–42, Florence, Italy. Association for Computational Linguistics.