SOURCE-LINKED INTELLIGENCE
When Evidence Conflicts: Reliability-aware Meta-review Generation
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 es
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-21T02:50:49.000Z
First collected: 2026-09-23T09:51:33.063Z. This is not the publication date.