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

ACL ARR 2025 May Submission4477 Authors

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 (LLMs) 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. 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.

Paper Type: Long

Research Area: Summarization

Research Area Keywords: Multi-document summarization, reinforcement learning, topic-guided reward, GRPO

Contribution Types: NLP engineering experiment

Languages Studied: English

Submission Number: 4477