Aggregating, Summarizing, and Restructuring News-Related Tweets into Compositions Using Deep Learning

Abstract

With the advent of microblogging platforms like Twitter, there has been a substantial shift toward digital media for getting acquainted with ongoing global issues. Although Twitter is an incredible source of information for real-time news, the information is widely scattered, opinionated, and unorganized, making it tedious for users to apprise themselves of the latest issues. Therefore, this study proposes a framework to automatically generate short news compositions from tweets utilizing state-of-the-art artificial intelligence techniques. The proposed framework scrapes tweets from authentic news Twitter handles, semantically analyzes and clusters them, predicts sentence ordering of the formed clusters, and summarizes the text of the clusters to produce structured compositions automatically. The generated compositions are further augmented with their corresponding sentiment scores to provide an overall perspective to the end-user toward the news topic in consideration. Evaluating the automatically generated compositions shows that the proposed framework is 77.5% efficient in generating quality compositions.