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arXiv · 2609.24028

When Evidence Conflicts: Reliability-aware Meta-review Generation

Xinzhe Wang·Fei Tao·Jiang Xie·Hong Yu·Ye Wang
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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.

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Xinzhe Wang, Fei Tao, Jiang Xie, Hong Yu, Ye Wang. 2026-09-21. When Evidence Conflicts: Reliability-aware Meta-review Generation. https://arxiv.org/abs/2609.24028

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