Topic-Guided Reinforcement Learning with LLMs for Enhancing Multi-Document Summarization

• URL: http://arxiv.org/abs/2509.09852v1
• Date: Thu, 11 Sep 2025 21:01:54 GMT
• Title: Topic-Guided Reinforcement Learning with LLMs for Enhancing Multi-Document Summarization
• Authors: Chuyuan Li, Austin Xu, Shafiq Joty, Giuseppe Carenini,
• Abstract summary: We propose a topic-guided reinforcement learning approach to improve content selection in Multi-Document Summarization.<n>We first show that explicitly prompting models with topic labels enhances the informativeness of the generated summaries.
• Score: 49.61589046694085
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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