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    <abstract>Generating coherent meta-reviews from multiple peer reviews is challenging when reviewer evidence conflicts and varies in reliability. Existing approaches typically formulate meta-review generation as a multi-document summarization task and aggregate reviewer feedback uniformly, making it difficult to determine which opinions should be prioritized under disagreement. In this paper, we study meta-review generation through reliability-aware evidence aggregation. Our framework first extracts aspect-level opinions from peer reviews and identifies conflicting evidence within each aspect. It then estimates opinion-level support and review-level quality to measure evidence reliability. Based on these signals, the framework assigns reliability-aware weights to reviewer feedback, enabling the generator to prioritize better-supported arguments while preserving diverse perspectives. Experiments demonstrate that our method consistently improves meta-review generation over strong baselines on both automatic and human evaluations, with clear gains in conflict recognition and resolution under high-conflict review scenarios. The code and implementation details are publicly available at https://github.com/Wangxz729/reliability-aware-meta-review.</abstract>
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%0 Conference Proceedings
%T When Evidence Conflicts: Reliability-aware Meta-review Generation
%A Wang, Xinzhe
%A Tao, Fei
%A Xie, Jiang
%A Yu, Hong
%A Wang, Ye
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F wang-etal-2026-evidence
%X Generating coherent meta-reviews from multiple peer reviews is challenging when reviewer evidence conflicts and varies in reliability. Existing approaches typically formulate meta-review generation as a multi-document summarization task and aggregate reviewer feedback uniformly, making it difficult to determine which opinions should be prioritized under disagreement. In this paper, we study meta-review generation through reliability-aware evidence aggregation. Our framework first extracts aspect-level opinions from peer reviews and identifies conflicting evidence within each aspect. It then estimates opinion-level support and review-level quality to measure evidence reliability. Based on these signals, the framework assigns reliability-aware weights to reviewer feedback, enabling the generator to prioritize better-supported arguments while preserving diverse perspectives. Experiments demonstrate that our method consistently improves meta-review generation over strong baselines on both automatic and human evaluations, with clear gains in conflict recognition and resolution under high-conflict review scenarios. The code and implementation details are publicly available at https://github.com/Wangxz729/reliability-aware-meta-review.
%U https://aclanthology.org/2026.findings-emnlp.746/
%P 15708-15723

Markdown (Informal)

[When Evidence Conflicts: Reliability-aware Meta-review Generation](https://aclanthology.org/2026.findings-emnlp.746/) (Wang et al., Findings 2026)

• When Evidence Conflicts: Reliability-aware Meta-review Generation (Wang et al., Findings 2026)

ACL

• Xinzhe Wang, Fei Tao, Jiang Xie, Hong Yu, and Ye Wang. 2026. When Evidence Conflicts: Reliability-aware Meta-review Generation. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 15708–15723, Budapest, Hungary. Association for Computational Linguistics.