AI 中文总结
针对大语言模型长文档摘要性能挑战，提出专家 - 编辑逐步提问多智能体方法，通过不同方面提问和提供修订线索完善摘要，经实验验证了该方法在长文档科学数据集上的有效性。
AI 中文摘要
尽管大语言模型在新闻摘要任务中展现出潜力，但在长文档摘要方面，因长度常超输入限制，性能仍具挑战。本文提出专家 - 编辑逐步提问多智能体方法，通过专家和编辑就内容不同方面提问并提供针对性修订线索，引导另一智能体完善摘要。在两个长文档科学数据集上实验，用公认自动指标评估结果，证明了该方法的有效性。
英文摘要
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.
Comments12 pages,3 figures,2 tables
