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        <title>Summarizing Multiple Documents with Conversational Structure for Meta-Review Generation</title>
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        <namePart type="given">Miao</namePart>
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    <abstract>We present PeerSum, a novel dataset for generating meta-reviews of scientific papers. The meta-reviews can be interpreted as abstractive summaries of reviews, multi-turn discussions and the paper abstract. These source documents have a rich inter-document relationship with an explicit hierarchical conversational structure, cross-references and (occasionally) conflicting information. To introduce the structural inductive bias into pre-trained language models, we introduce RAMMER (Relationship-aware Multi-task Meta-review Generator), a model that uses sparse attention based on the conversational structure and a multi-task training objective that predicts metadata features (e.g., review ratings). Our experimental results show that RAMMER outperforms other strong baseline models in terms of a suite of automatic evaluation metrics. Further analyses, however, reveal that RAMMER and other models struggle to handle conflicts in source documents, suggesting meta-review generation is a challenging task and a promising avenue for further research.</abstract>
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%0 Conference Proceedings
%T Summarizing Multiple Documents with Conversational Structure for Meta-Review Generation
%A Li, Miao
%A Hovy, Eduard
%A Lau, Jey
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Findings of the Association for Computational Linguistics: EMNLP 2023
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F li-etal-2023-summarizing
%X We present PeerSum, a novel dataset for generating meta-reviews of scientific papers. The meta-reviews can be interpreted as abstractive summaries of reviews, multi-turn discussions and the paper abstract. These source documents have a rich inter-document relationship with an explicit hierarchical conversational structure, cross-references and (occasionally) conflicting information. To introduce the structural inductive bias into pre-trained language models, we introduce RAMMER (Relationship-aware Multi-task Meta-review Generator), a model that uses sparse attention based on the conversational structure and a multi-task training objective that predicts metadata features (e.g., review ratings). Our experimental results show that RAMMER outperforms other strong baseline models in terms of a suite of automatic evaluation metrics. Further analyses, however, reveal that RAMMER and other models struggle to handle conflicts in source documents, suggesting meta-review generation is a challenging task and a promising avenue for further research.
%R 10.18653/v1/2023.findings-emnlp.472
%U https://aclanthology.org/2023.findings-emnlp.472
%U https://doi.org/10.18653/v1/2023.findings-emnlp.472
%P 7089-7112

Markdown (Informal)

[Summarizing Multiple Documents with Conversational Structure for Meta-Review Generation](https://aclanthology.org/2023.findings-emnlp.472) (Li et al., Findings 2023)

• Summarizing Multiple Documents with Conversational Structure for Meta-Review Generation (Li et al., Findings 2023)

ACL

• Miao Li, Eduard Hovy, and Jey Lau. 2023. Summarizing Multiple Documents with Conversational Structure for Meta-Review Generation. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 7089–7112, Singapore. Association for Computational Linguistics.