Aspect-oriented Consumer Health Answer Summarization

TL;DR

This paper introduces an aspect-based summarization approach for health answers in community Q&A forums, creating a dataset and a pipeline that improves the diversity and relevance of summaries for healthcare queries.

Contribution

It formalizes annotation guidelines, provides a new dataset, and develops a multi-stage summarization pipeline leveraging state-of-the-art models for health answer summarization.

Findings

Summaries effectively capture relevant health information and diverse solutions.

The approach outperforms single-answer summaries in content coverage.

Human evaluation shows high relevance and comprehensiveness of generated summaries.

Abstract

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.