MIDAS: A Multi-LLM Framework for Adaptive Text Summarization

A recent paper on arXiv (ID: 2608.04307) presents MIDAS (Multi-LLM Iterative Data-Adaptive Summarization), a framework aimed at tackling the complexities of summarizing enterprise texts. This cross-type submission emphasizes that while summarization may seem simple, practical scenarios like legal documents, support tickets, and incident reports necessitate strict adherence to specific guidelines, formats, and organizational standards. Developing prompts that consistently fulfill these criteria is time-consuming and requires ongoing adjustments as needs change. Current methods for automated prompt optimization rely on LLM critique-driven refinement but are hindered by static prompts that lack adaptability. MIDAS enhances this approach by integrating data-driven pattern recognition and personalized use-case adaptations, utilizing multiple LLMs to iteratively refine prompts according to data patterns, thereby minimizing manual effort and enhancing flexibility. The full paper can be accessed at https://arxiv.org/abs/2608.04307.