用主题引导强化学习，提升多文档摘要的信息量与相关性
Topic-Guided Reinforcement Learning with LLMs for Enhancing Multi-Document Summarization
- 用主题标签显式提示模型，增强摘要信息量
- 在GRPO框架中引入主题匹配奖励，优化内容选择
- 在Multi-News和Multi-XScience上优于主流基线
多文档摘要（MDS）的核心挑战在于如何有效整合多源信息，同时保持连贯性和主题相关性。尽管大语言模型在单文档摘要中表现优异，但在MDS任务上仍有提升空间。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。首先，我们发现通过显式提示模型使用主题标签，能显著提升生成摘要的信息量。基于此，我们在组相对策略优化（GRPO）框架内设计了一种新的主题奖励机制，用于衡量生成摘要与原始文档之间的主题一致性。在Multi-News和Multi-XScience数据集上的实验表明，该方法始终优于多个强基线模型，验证了利用主题线索在MDS中的有效性。
原文摘要 · Abstract (English)
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.
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