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        <title>A Sentiment Consolidation Framework for Meta-Review Generation</title>
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        <namePart type="given">Han</namePart>
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        <namePart type="given">Eduard</namePart>
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            <namePart type="given">Vivek</namePart>
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    <abstract>Modern natural language generation systems with Large Language Models (LLMs) exhibit the capability to generate a plausible summary of multiple documents; however, it is uncertain if they truly possess the capability of information consolidation to generate summaries, especially on documents with opinionated information. We focus on meta-review generation, a form of sentiment summarisation for the scientific domain. To make scientific sentiment summarization more grounded, we hypothesize that human meta-reviewers follow a three-layer framework of sentiment consolidation to write meta-reviews. Based on the framework, we propose novel prompting methods for LLMs to generate meta-reviews and evaluation metrics to assess the quality of generated meta-reviews. Our framework is validated empirically as we find that prompting LLMs based on the framework — compared with prompting them with simple instructions — generates better meta-reviews.</abstract>
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    <identifier type="doi">10.18653/v1/2024.acl-long.547</identifier>
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        <date>2024-08</date>
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            <start>10158</start>
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%0 Conference Proceedings
%T A Sentiment Consolidation Framework for Meta-Review Generation
%A Li, Miao
%A Lau, Jey Han
%A Hovy, Eduard
%Y Ku, Lun-Wei
%Y Martins, Andre
%Y Srikumar, Vivek
%S Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand
%F li-etal-2024-sentiment
%X Modern natural language generation systems with Large Language Models (LLMs) exhibit the capability to generate a plausible summary of multiple documents; however, it is uncertain if they truly possess the capability of information consolidation to generate summaries, especially on documents with opinionated information. We focus on meta-review generation, a form of sentiment summarisation for the scientific domain. To make scientific sentiment summarization more grounded, we hypothesize that human meta-reviewers follow a three-layer framework of sentiment consolidation to write meta-reviews. Based on the framework, we propose novel prompting methods for LLMs to generate meta-reviews and evaluation metrics to assess the quality of generated meta-reviews. Our framework is validated empirically as we find that prompting LLMs based on the framework — compared with prompting them with simple instructions — generates better meta-reviews.
%R 10.18653/v1/2024.acl-long.547
%U https://aclanthology.org/2024.luhme-long.547/
%U https://doi.org/10.18653/v1/2024.acl-long.547
%P 10158-10177

Markdown (Informal)

[A Sentiment Consolidation Framework for Meta-Review Generation](https://aclanthology.org/2024.luhme-long.547/) (Li et al., ACL 2024)

• A Sentiment Consolidation Framework for Meta-Review Generation (Li et al., ACL 2024)

ACL

• Miao Li, Jey Han Lau, and Eduard Hovy. 2024. A Sentiment Consolidation Framework for Meta-Review Generation. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 10158–10177, Bangkok, Thailand. Association for Computational Linguistics.