用专家与编辑分步提问，提升长文档摘要质量
A Stepwise Questioning Expert-Editor Multi-Agent Framework for Long-Document Summarization
- 专家与编辑分步提问，引导模型逐层优化摘要
- 在两个科学数据集上显著优于基线方法
- 适合需要精准长文摘要的研究者使用
尽管大语言模型在新闻摘要任务中展现出潜力，但在处理长文档时仍面临挑战，因其长度常超出输入限制。本文提出一种专家-编辑分步提问的多智能体框架，通过专家和编辑从不同角度提出问题并提供修订线索，指导另一个智能体逐步完善摘要。我们在两个代表性的长篇科学数据集上进行实验，并采用广泛认可的自动评估指标进行评测。结果表明，该方法在长文档摘要任务中具有显著有效性。
原文摘要 · Abstract (English)
Although large language models (LLMs) have shown promising potential in news summarization tasks, their performance on long-document summarization remains challenging as their length often exceeds the input limits. As the agent investment, which provide possibility to improve the inherent capabilities of LLMs. To enhance the effectiveness of long-document summarization based on LLMs, this paper proposes an expert-editor stepwise questioning multi-agent method, in which the expert and the editor guide another agent to refine the summary by posing questions on different aspects of the content and providing targeted clues for revision. We conducted experiments on two representative long-document scientific datasets and evaluated the results through widely recognized automatic metrics. The results demonstrated the effectiveness of our method.
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