基于LLM的主题引导强化学习在多文档摘要中的应用
Topic-Guided Reinforcement Learning with LLMs for Enhancing Multi-Document Summarization
AI总结：
本文提出一种主题引导的强化学习方法，通过在GRPO框架中引入主题奖励以衡量生成摘要与源文档的主题对齐度，有效改进了多文档摘要的内容选择，并在Multi-News等数据集上持续优于强基线。
摘要：
多文档摘要（MDS）的一个关键挑战是在保持连贯性和主题相关性的同时，有效地整合来自多个来源的信息。尽管大型语言模型在单文档摘要中表现出色，但其在MDS上的性能仍有提升空间。本文提出一种主题引导的强化学习方法，以改进MDS中的内容选择。我们首先证明，使用主题标签显式提示模型能增强生成摘要的信息量。基于此发现，我们在组相对策略优化（GRPO）框架内提出一种新颖的主题奖励，用于衡量生成摘要与源文档间的主题对齐度。在Multi-News和Multi-XScience数据集上的实验结果表明，该方法持续优于强基线，凸显了在MDS中利用主题线索的有效性。
英文摘要：
A key challenge in Multi-Document Summarization (MDS) is effectively integrating information from multiple sources while maintaining coherence and topical relevance. While Large Language Models have shown impressive results in single-document summarization, their performance on MDS still leaves room for improvement. In this paper, we propose a topic-guided reinforcement learning approach to improve content selection in MDS. We first show that explicitly prompting models with topic labels enhances the informativeness of the generated summaries. Building on this insight, we propose a novel topic reward within the Group Relative Policy Optimization (GRPO) framework to measure topic alignment between the generated summary and source documents. Experimental results on the Multi-News and Multi-XScience datasets demonstrate that our method consistently outperforms strong baselines, highlighting the effectiveness of leveraging topical cues in MDS.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
