面向方面的消费者健康答案摘要生成
Aspect-oriented Consumer Health Answer Summarization
AI总结：
针对健康社区问答中单一高票答案遗漏多元信息的问题，本文构建方面级人工摘要数据集，并提出检索、方面分类与抽象式摘要相结合的多阶段流水线。
摘要：
社区问答（Community Question-Answering，CQA）论坛彻底改变了人们寻求信息的方式，尤其是与其医疗保健需求相关的信息，人们将信任寄托于公众的集体智慧。然而，针对单个问题可能存在多个答案，这使得人们难以掌握与特定健康关切相关的关键信息。通常，CQA论坛会以一个得票最高的答案作为每个问题的代表性摘要。但是，单个答案会忽略其他回复中经常提供的替代解决方案和其他信息。我们的研究聚焦于基于方面的健康答案摘要生成，以解决这一局限。按照建议、信息、个人经历和问题等不同方面对回复进行摘要，可以提升平台的可用性。我们制定了多阶段标注指南，并贡献了一个独特数据集，其中包含基于方面的人工撰写健康答案摘要。我们利用该数据集，在对若干最先进模型进行面向任务的微调基础上，构建了自动化的多方面答案摘要流水线。该流水线利用问题相似性检索相关答案句，随后将其分类到适当的方面类型。接着，我们采用若干近期的抽象式摘要模型生成基于方面的摘要。最后，我们给出了全面的人工分析，并发现我们的摘要在捕捉相关内容和广泛解决方案方面排名很高。
英文摘要：
Community Question-Answering (CQA) forums have revolutionized how people seek information, especially those related to their healthcare needs, placing their trust in the collective wisdom of the public. However, there can be several answers in response to a single query, which makes it hard to grasp the key information related to the specific health concern. Typically, CQA forums feature a single top-voted answer as a representative summary for each query. However, a single answer overlooks the alternative solutions and other information frequently offered in other responses. Our research focuses on aspect-based summarization of health answers to address this limitation. Summarization of responses under different aspects such as suggestions, information, personal experiences, and questions can enhance the usability of the platforms. We formalize a multi-stage annotation guideline and contribute a unique dataset comprising aspect-based human-written health answer summaries. We build an automated multi-faceted answer summarization pipeline with this dataset based on task-specific fine-tuning of several state-of-the-art models. The pipeline leverages question similarity to retrieve relevant answer sentences, subsequently classifying them into the appropriate aspect type. Following this, we employ several recent abstractive summarization models to generate aspect-based summaries. Finally, we present a comprehensive human analysis and find that our summaries rank high in capturing relevant content and a wide range of solutions.
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。
