发表机构
Chongqing University of Posts and Telecommunications; NewsBreak（重庆邮电大学; NewsBreak）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对评审证据冲突问题，提出可靠性感知的元评论生成框架，通过提取方面级意见、识别冲突并估计证据可靠性，分配权重以优先支持度高的论点，实验验证了其有效性。
AI 中文摘要
当评审证据相互冲突且可靠性各异时，从多篇同行评审中生成连贯的元评论具有挑战性。现有方法通常将元评论生成视为多文档摘要任务，并统一聚合评审反馈，这使得在意见分歧时难以确定应优先考虑哪些观点。在本文中，我们通过可靠性感知的证据聚合来研究元评论生成。我们的框架首先从同行评审中提取方面级意见，并识别每个方面内的冲突证据。然后，它估计意见级支持度和评审级质量以衡量证据可靠性。基于这些信号，框架为评审反馈分配可靠性感知的权重，使生成器能够优先考虑支持度更高的论点，同时保留多样化的视角。实验表明，我们的方法在自动评估和人工评估中均持续优于强基线，显著提升了元评论生成效果，并在高冲突评审场景下展现出明显的冲突识别与解决能力。代码和实现细节已公开于该https URL。
英文摘要
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.
