A Stepwise Questioning Expert-Editor Multi-Agent Framework for Long-Document Summarization

2026-07-11 · Source: Artificial Intelligence · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Natural Language Processing · Depth: Expert, quick

Summary

A novel "Stepwise Questioning Expert-Editor Multi-Agent Framework" has been proposed to enhance long-document summarization using large language models (LLMs). This framework addresses the challenge of LLMs' input length limitations, which typically hinder their performance on extensive texts. The method employs a multi-agent system where an "expert" agent and an "editor" agent collaboratively guide a third agent. These guiding agents refine summaries by posing specific questions about content aspects and offering targeted clues for revision. Experiments were conducted on two representative long-document scientific datasets, and the framework's effectiveness was validated using widely recognized automatic metrics. This approach demonstrates a promising direction for improving LLM capabilities in processing and summarizing lengthy documents.

Key takeaway

For NLP Engineers developing long-document summarization solutions, consider integrating multi-agent frameworks to overcome LLM input limitations. You should explore designing specialized agents, like the proposed expert and editor, to iteratively refine summaries through guided questioning and targeted feedback. This approach can significantly enhance summary quality for extensive texts, moving beyond simple prompt engineering.

Key insights

The framework uses expert and editor agents to iteratively refine LLM-generated long-document summaries through questioning and targeted clues.

Principles

• Iterative refinement improves LLM summarization.
• Multi-agent systems enhance LLM capabilities.
• Guided questioning refines content aspects.

Method

The method involves expert and editor agents guiding another agent to refine summaries. This guidance occurs through stepwise questioning on content aspects and providing targeted revision clues.

In practice

• Apply multi-agent systems for complex NLP.
• Use iterative questioning for content refinement.
• Design agents for specific editorial roles.

Topics

• Long-Document Summarization
• Large Language Models
• Multi-Agent Systems
• Expert-Editor Framework
• Scientific Datasets
• Natural Language Processing

Best for: Research Scientist, AI Engineer, AI Scientist, Machine Learning Engineer, NLP Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence.