PERCS：基于人物的可控生物医学摘要数据集
PERCS: Persona-Guided Controllable Biomedical Summarization Dataset
AI总结：
PERCS数据集通过针对不同医学素养角色的可控摘要，提升生物医学信息的可及性与准确性。
摘要：
自动医疗文本简化在提高健康素养方面起着关键作用，通过使复杂的生物医学研究对不同读者群体可及。然而，现有资源大多假设单一通用受众，忽视了用户群体之间在医学素养和信息需求上的广泛差异。为解决这一限制，我们引入PERCS（基于人物的可控摘要），一个包含生物医学摘要及其针对四个角色的摘要的数据集：普通公众、预医学学生、非医学研究人员和医学专家。这些角色代表了不同的医学素养水平和信息需求，强调了需要针对特定受众进行摘要的必要性。PERCS中的每个摘要均由医生审核，以确保事实准确性及角色匹配性，使用详细的错误分类法。技术验证显示，不同角色在可读性、词汇和内容深度上存在明显差异。除了描述该数据集，我们还利用自动评估指标对四个大型语言模型在PERCS上的表现进行了基准测试，评估内容的全面性、可读性和准确性，为未来研究建立了基准结果。该数据集、标注指南和评估材料已公开，以支持针对特定角色的沟通和可控生物医学摘要研究。
英文摘要：
Automatic medical text simplification plays a key role in improving health literacy by making complex biomedical research accessible to diverse readers. However, most existing resources assume a single generic audience, overlooking the wide variation in medical literacy and information needs across user groups. To address this limitation, we introduce PERCS (Persona-guided Controllable Summarization), a dataset of biomedical abstracts paired with summaries tailored to four personas: Laypersons, Premedical Students, Non-medical Researchers, and Medical Experts. These personas represent different levels of medical literacy and information needs, emphasizing the need for targeted, audience-specific summarization. Each summary in PERCS was reviewed by physicians for factual accuracy and persona alignment using a detailed error taxonomy. Technical validation shows clear differences in readability, vocabulary, and content depth across personas. Along with describing the dataset, we benchmark four large language models on PERCS using automatic evaluation metrics that assess comprehensiveness, readability, and faithfulness, establishing baseline results for future research. The dataset, annotation guidelines, and evaluation materials are publicly available to support research on persona-specific communication and controllable biomedical summarization.
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。
