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        <title>Comparative Opinion Summarization via Collaborative Decoding</title>
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    <abstract>Opinion summarization focuses on generating summaries that reflect popular subjective information expressed in multiple online reviews. While generated summaries offer general and concise information about a particular hotel or product, the information may be insufficient to help the user compare multiple different choices. Thus, the user may still struggle with the question “Which one should I pick?” In this paper, we propose the comparative opinion summarization task, which aims at generating two contrastive summaries and one common summary from two different candidate sets of reviews. We develop a comparative summarization framework CoCoSum, which consists of two base summarization models that jointly generate contrastive and common summaries. Experimental results on a newly created benchmark CoCoTrip show that CoCoSum can produce higher-quality contrastive and common summaries than state-of-the-art opinion summarization models. The dataset and code are available at https://github.com/megagonlabs/cocosum</abstract>
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%0 Conference Proceedings
%T Comparative Opinion Summarization via Collaborative Decoding
%A Iso, Hayate
%A Wang, Xiaolan
%A Angelidis, Stefanos
%A Suhara, Yoshihiko
%Y Muresan, Smaranda
%Y Nakov, Preslav
%Y Villavicencio, Aline
%S Findings of the Association for Computational Linguistics: ACL 2022
%D 2022
%8 May
%I Association for Computational Linguistics
%C Dublin, Ireland
%F iso-etal-2022-comparative
%X Opinion summarization focuses on generating summaries that reflect popular subjective information expressed in multiple online reviews. While generated summaries offer general and concise information about a particular hotel or product, the information may be insufficient to help the user compare multiple different choices. Thus, the user may still struggle with the question “Which one should I pick?” In this paper, we propose the comparative opinion summarization task, which aims at generating two contrastive summaries and one common summary from two different candidate sets of reviews. We develop a comparative summarization framework CoCoSum, which consists of two base summarization models that jointly generate contrastive and common summaries. Experimental results on a newly created benchmark CoCoTrip show that CoCoSum can produce higher-quality contrastive and common summaries than state-of-the-art opinion summarization models. The dataset and code are available at https://github.com/megagonlabs/cocosum
%R 10.18653/v1/2022.findings-acl.261
%U https://aclanthology.org/2022.findings-acl.261
%U https://doi.org/10.18653/v1/2022.findings-acl.261
%P 3307-3324

Markdown (Informal)

[Comparative Opinion Summarization via Collaborative Decoding](https://aclanthology.org/2022.findings-acl.261) (Iso et al., Findings 2022)

• Comparative Opinion Summarization via Collaborative Decoding (Iso et al., Findings 2022)

ACL

• Hayate Iso, Xiaolan Wang, Stefanos Angelidis, and Yoshihiko Suhara. 2022. Comparative Opinion Summarization via Collaborative Decoding. In Findings of the Association for Computational Linguistics: ACL 2022, pages 3307–3324, Dublin, Ireland. Association for Computational Linguistics.