Multi-LLM Text Summarization

The paper presents a novel framework called Multi-LLM for text summarization, which utilizes multiple large language models (LLMs) to enhance the quality of summaries, particularly for lengthy documents. This framework addresses the limitations of single LLMs by employing two strategies: centralized and decentralized. In the centralized approach, several LLMs create candidate summaries, with a central LLM evaluating and selecting the best one, aiming to balance computational efficiency and output quality. The decentralized strategy involves multiple LLMs in both generating and evaluating summaries to reach a consensus for a more comprehensive outcome. The process includes a two-stage method: initially breaking down the text into smaller segments for summarization, followed by re-summarizing these intermediate outputs to form a cohesive final summary. The research demonstrates that the Multi-LLM framework significantly surpasses traditional single LLM methods in quality metrics such as ROUGE and BLEU scores, indicating better handling of information distribution and content balancing. While the results are promising, suggesting potential for delivering superior summaries of complex texts, the authors acknowledge the need for further refinement, especially in exploring additional topological strategies and optimizing prompt engineering. They encourage ongoing exploration of integrating various LLMs and testing across broader domains.