Topic-Guided Reinforcement Learning with LLMs for Enhancing Multi-Document Summarization
This addresses the challenge of effectively integrating information from multiple sources while maintaining coherence and topical relevance in multi-document summarization, representing an incremental improvement.
The paper tackles the problem of improving content selection in multi-document summarization by proposing a topic-guided reinforcement learning approach, which outperforms strong baselines on Multi-News and Multi-XScience datasets.
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.