Automated Summarization of Software Documents: An LLM-based Multi-Agent Approach

Large Language Models (LLMs) and LLM-based Multi-Agent Sys-tems (MAS) are revolutionizing software engineering (SE) by advancing automation, decision-making, and knowledge processing. Their recent application to SE tasks has already shown promising results. In this paper, we focus on summarization as a key application area. We present Metagente, an LLM-based MAS designed to generate concise and accurate summaries ofsoftware documentation. Metagente employs a Teacher–Student architecture where multiple LLM agents collaborate to enhance relevance and precision of produced summaries. An empirical evaluation on real-world datasets demonstrates Metagente’s effectiveness in streamlining workflows, outperforming theconsidered baselines. The evaluation provides evidence that Metagente improves summarization for requirements analysis and technical documentation. Our findings underscore the transformative potential of these technologies inSE, while identifying challenges and future research directions for their seamless integration.