Summary of Aspect-oriented Consumer Health Answer Summarization, by Rochana Chaturvedi et al.

Aspect-oriented Consumer Health Answer Summarization

by Rochana Chaturvedi, Abari Bhattacharya, Shweta Yadav

First submitted to arxiv on: 10 May 2024

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Summary difficulty	Written by	Summary
High	Paper authors	High Difficulty Summary Read the original abstract here
Medium	GrooveSquid.com (original content)	Medium Difficulty Summary A novel approach to community question-answering (CQA) forums is proposed, focusing on aspect-based summarization of health answers. Current CQA platforms often rely on a single top-voted answer per query, overlooking alternative solutions and information in other responses. To address this limitation, the research formalizes a multi-stage annotation guideline and contributes a unique dataset for human-written health answer summaries, categorized by aspects such as suggestions, information, personal experiences, and questions. An automated pipeline is built using task-specific fine-tuning of state-of-the-art models, leveraging question similarity to retrieve relevant answer sentences and classifying them into aspect types. Abstractive summarization models are employed to generate summaries, which are found to rank high in capturing relevant content and diverse solutions.
Low	GrooveSquid.com (original content)	Low Difficulty Summary A new way to help people get answers from online forums is being explored. Right now, most health-related forums show one top answer for each question, but that can hide other helpful information. To fix this, researchers created a special set of guidelines and a unique dataset for summarizing health answers in different ways, like suggestions or personal experiences. They also built an automated system that uses machine learning models to group similar answer sentences together and generate summaries. The results show that these summaries are great at capturing important details and providing many possible solutions.