Exploring Unsupervised Learning Towards Extractive Summarization of User Reviews

Abstract

Mobile app reviews are important as a crowdsource to improve the quality of these softwares. App stores, which have app reviews, provide a wealth of information derived from users. These information help developers to fix bugs and implement new features desired by users. Despite the reviews usefulness, one of the challenges of application developers is the huge number of reviews published daily, making manual analysis laborious. Hence, the delay in satisfying users may influence the loss of customers. Current researches into this topic have adopted a supervised approach to classify the reviews of the users. In this paper, we used an unsupervised approach to categorize the reviews aiming to generate a summary of the main bugs and new features pointed by users, assisting the application developers to improve the quality their apps. We evaluated the approach empirically and obtained promising results to generate user reviews summaries.