标题： 利用分层组织进行医学多文档摘要 显示英文标题

标题： Leveraging Hierarchical Organization for Medical Multi-document Summarization

摘要： 医学多文档摘要（MDS）是一项复杂的任务，需要有效管理跨文档的关系。 本文研究了在MDS的输入中引入分层结构是否能够提高模型在组织和上下文化信息方面的能力，与传统的扁平摘要方法相比。 我们研究了两种在三个大型语言模型（LLMs）中引入分层组织的方法，并使用自动化指标、基于模型的指标以及领域专家对偏好、易懂性、清晰度、复杂性、相关性、覆盖范围、事实性和连贯性的评估，对生成的摘要进行了全面评估。 我们的结果表明，人类专家更倾向于模型生成的摘要而非人工撰写的摘要。 分层方法通常能保持信息的事实性、覆盖范围和连贯性，同时提高人们对摘要的偏好。 此外，我们还检查了来自GPT-4的模拟判断是否与人类判断一致，发现在更客观的评估方面有更高的一致性。 我们的研究结果表明，分层结构可以在保持内容覆盖范围的同时，提高模型生成的医学摘要的清晰度，为提高生成摘要的人类偏好提供了一种实用的方法。 显示英文摘要

摘要： Medical multi-document summarization (MDS) is a complex task that requires effectively managing cross-document relationships. This paper investigates whether incorporating hierarchical structures in the inputs of MDS can improve a model's ability to organize and contextualize information across documents compared to traditional flat summarization methods. We investigate two ways of incorporating hierarchical organization across three large language models (LLMs), and conduct comprehensive evaluations of the resulting summaries using automated metrics, model-based metrics, and domain expert evaluation of preference, understandability, clarity, complexity, relevance, coverage, factuality, and coherence. Our results show that human experts prefer model-generated summaries over human-written summaries. Hierarchical approaches generally preserve factuality, coverage, and coherence of information, while also increasing human preference for summaries. Additionally, we examine whether simulated judgments from GPT-4 align with human judgments, finding higher agreement along more objective evaluation facets. Our findings demonstrate that hierarchical structures can improve the clarity of medical summaries generated by models while maintaining content coverage, providing a practical way to improve human preference for generated summaries.