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.

我们的研究关注于健康答案的基于方面的总结，以解决信息不全的问题。我们提出了一个多阶段的注释指南和一个独特的数据集，包括基于方面的人工编写的健康答案总结。我们利用该数据集构建了一个自动化的多方面答案总结流程，使用了几种最先进的模型进行特定任务的微调。该流程利用问题相似性检索相关的回答句子，然后将其分类为适当的方面类型。接下来，我们使用几种最新的抽象总结模型生成基于方面的总结。最后，我们进行了全面的人工分析，发现我们的总结在捕捉相关内容和各种解决方案方面排名靠前。