面向科学合成的局部到全局句子级图重排序
Local-to-Global Sentence-Level Graph Reranking for Scientific Synthesis
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中文总结 AI 辅助
针对现有段落级重排器忽略候选间关系的局限，提出LoG-Reranker框架，通过局部评分结合句子图全局优化排序，在基准上表现优于竞争方法，提升科学合成质量。
中文摘要 AI 辅助
检索增强型科学合成旨在通过整合多篇论文的信息，生成全面且有依据的响应以回答复杂的研究问题。由于生成器仅能合成重排器所选并组织的信息，生成的合成结果质量关键取决于重排后的结果。然而，大多数重排器在段落级别运行，导致关键的方法、实证及比较信息被埋没在冗长且扁平的上下文中，削弱了生成主张的依据性。此外，现有重排器主要依赖独立的查询-候选评分，忽略了科学候选间的互补、上下文及对比关系，限制了信息覆盖范围和合成结果的全面性。为解决这些局限，我们提出LoG-Reranker，一种面向科学合成的局部到全局句子级图重排序框架。LoG-Reranker执行角色感知的局部评分以识别细粒度、与查询相关的句子，随后在候选集上的句子图中建模它们的关系，以全局优化句子排序。将排名最高的句子及其相连邻居组织成结构化输入上下文，供生成器生成更具依据性和全面性的结果。在科学合成和重排序基准上的实验表明，LoG-Reranker始终优于具有竞争力的重排器，产生更可靠的排名并提升生成合成结果的质量。
英文摘要
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.
