Long story short: finding health advice with informative summaries on health social media.

Purpose: Whether automatically generated summaries of health social media can aid users in managing their diseases appropriately is an important question. The purpose of this paper is to introduce a novel text summarization approach for acquiring the most informative summaries from online patient posts accurately and effectively. Design/methodology/approach: The data set regarding diabetes and HIV posts was, respectively, collected from two online disease forums. The proposed summarizer is based on the graph-based method to generate summaries by considering social network features, text sentiment and sentence features. Representative health-related summaries were identified and summarization performance as well as user judgments were analyzed. Findings: The findings show that awarding sentences without using all the incorporating features decreases summarization performance compared with the classic summarization method and comparison approaches. The proposed summarizer significantly outperformed the comparison baseline. Originality/value: This study contributes to the literature on health knowledge management by analyzing patients' experiences and opinions through the health summarization model. The research additionally develops a new mindset to design abstractive summarization weighting schemes from the health user-generated content. [ABSTRACT FROM AUTHOR]