利用层次化组织进行医学多文档摘要
Leveraging Hierarchical Organization for Medical Multi-document Summarization
AI总结：
本文研究在医学多文档摘要输入中引入层次化结构，以提升模型跨文档组织和情境化信息的能力，实验证明该方法能保持事实性、覆盖度与连贯性，并提高人类对摘要的偏好。
摘要：
医学多文档摘要（MDS）是一项复杂的任务，需要有效地管理跨文档关系。本文研究了在MDS的输入中结合层次化结构，是否能够提升模型跨文档组织和情境化信息的能力，并与传统的扁平化摘要方法进行了比较。我们探讨了在三个大型语言模型（LLMs）中引入层次化组织的两种方式，并使用自动化指标、基于模型的指标，以及领域专家对偏好、可理解性、清晰度、复杂性、相关性、覆盖度、事实性、连贯性的评估，对生成的摘要进行了全面评估。结果显示，人类专家更偏好模型生成的摘要，而非人工编写的摘要。层次化方法通常能保持信息的事实性、覆盖度和连贯性，同时也提高了人类对摘要的偏好。此外，我们检验了来自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维度上具有更高的一致性。研究结果表明，层次化结构能够提升模型生成的医学摘要的清晰度，同时保持内容覆盖度，为提升人类对生成摘要的偏好提供了实用方法。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
