Local-to-Global Sentence-Level Graph Reranking for Scientific Synthesis

2026-08-31

AI Summary

为解决科学综合中信息覆盖不足和生成内容不全面的问题，提出LoG-Reranker方法，通过局部到全局的句子级图重排序来优化信息整合。

Abstract

Retrieval-augmented scientific synthesis aims to answer complex research questions by integrating information from multiple papers into comprehensive and well-grounded responses. Since the generator can only synthesize the information selected and organized by the reranker, the quality of the generated synthesis depends critically on the reranked results. However, most rerankers operate at the passage level, which leaves key methodological, empirical, and comparative information buried in long and flat contexts, weakening the grounding of generated claims. Moreover, existing rerankers mainly rely on independent query-candidate scoring which overlooks complementary, contextual, and contrasting relations across scientific candidates, limiting information coverage and the comprehensiveness of the resulting synthesis. To address these limitations, we propose LoG-Reranker, a local-to-global sentence-level graph reranking framework for scientific synthesis. LoG-Reranker performs role-aware local scoring to identify fine-grained, query-relevant sentences and then models their relations on a sentence graph across the candidate set to globally refine sentence rankings. Top-ranked sentences and their connected neighbors are organized into a structured input context for generator to produce more grounded and comprehensive synthesis.Extensive experiments on scientific synthesis and reranking benchmarks show that LoG-Reranker consistently outperforms competitive rerankers, yielding more reliable rankings and improving the quality of generated synthesis.

Problem

• retrieval-augmented scientific synthesis
• reranking
• information coverage
• comprehensive synthesis

Innovation

• Local-to-Global
• Sentence-Level Graph Reranking
• Scientific Synthesis
• Role-Aware Scoring
• Structured Input Context