# MIDAS: Multi-LLM Iterative Data-Adaptive Summarization

arXiv: https://arxiv.org/abs/2608.04307 · HTML: https://mlsanity.com/paper/2608.04307/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 20 references that are in the map.
- First posted on arXiv 5 Aug 2026.

## Builds on (most cited first, 14 of 14)

- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [A Survey of Large Language Models](https://mlsanity.com/paper/2303.18223/) (2023)
- [Large Language Models Are Human-Level Prompt Engineers](https://mlsanity.com/paper/2211.01910/) (2022)
- [Large Language Models as Optimizers](https://mlsanity.com/paper/2309.03409/) (2023)
- [LaMP: When Large Language Models Meet Personalization](https://mlsanity.com/paper/2304.11406/) (2023)
- [CoLLM: Integrating Collaborative Embeddings into Large Language Models for Recommendation](https://mlsanity.com/paper/2310.19488/) (2023)
- [PersonalLLM: Tailoring LLMs to Individual Preferences](https://mlsanity.com/paper/2409.20296/) (2024)
- [Integrating Summarization and Retrieval for Enhanced Personalization via Large Language Models](https://mlsanity.com/paper/2310.20081/) (2023)
- [CriSPO: Multi-Aspect Critique-Suggestion-guided Automatic Prompt Optimization for Text Generation](https://mlsanity.com/paper/2410.02748/) (2024)
- [PERSOMA: PERsonalized SOft ProMpt Adapter Architecture for Personalized Language Prompting](https://mlsanity.com/paper/2408.00960/) (2024)
- [A Comparative Study of Quality Evaluation Methods for Text Summarization](https://mlsanity.com/paper/2407.00747/) (2024)
- [ProRefine: Inference-Time Prompt Refinement with Textual Feedback](https://mlsanity.com/paper/2506.05305/) (2025)
- [ZERA: Zero-init Instruction Evolving Refinement Agent -- From Zero Instructions to Structured Prompts via Principle-based Optimization](https://mlsanity.com/paper/2509.18158/) (2025)
- [PMPO: Probabilistic Metric Prompt Optimization for Small and Large Language Models](https://mlsanity.com/paper/2505.16307/) (2025)

## Abstract (arXiv)

Text summarization is deceptively difficult. While condensing information seems straightforward, real-world enterprise summarization of support tickets, legal documents, incident reports, and more, demands strict adherence to domain-specific guidelines, output formats, and organizational conventions. Crafting prompts that reliably satisfy these constraints is labor-intensive, requiring significant human expertise and continuous maintenance as requirements evolve. Existing automated prompt optimization methods reduce this burden through Large Language Model (LLM) critique-driven refinement, yet remain limited by static prompts that cannot adapt to the diversity of summary applications. We propose Multi-LLM Iterative Data-Adaptive Summarization (MIDAS), a multi-LLM framework that extends this paradigm with data-driven pattern learning and use-case-specific personalization, enabling automatic adaptation to different summarization requirements without manual prompt engineering. Applied to enterprise customer ticket summarization across five output formats, MIDAS achieves the strongest overall performance against state-of-the-art critique-driven optimization frameworks such as CriSPO and ZERA, improving ROUGE-1 by up to 11.0%, ROUGE-2 by up to 18.2%, and ROUGE-L by up to 8.0%, while consistently improving BERTScore F1 across all formats and output types. We additionally demonstrate cross-model and cross-domain generalization through multi-LLM configurations and finance-domain summarization benchmarks.