On-demand recent personal tweets summarization on mobile devices

• Main Authors: Chin, Jin Yao, Bhowmick, Sourav S., Jatowt, Adam
• Other Authors: School of Computer Science and Engineering
• Format: Article
• Language: English
• Published: 2020
• Subjects: Engineering::Computer science and engineering; Summarization; Statistics
• Online Access: https://hdl.handle.net/10356/144489
• Institution: Nanyang Technological University

Tweets summarization aims to find a group of representative tweets for a specific set of input tweets or a given topic. In recent times, there have been several research efforts toward devising a variety of techniques to summarize tweets in Twitter. However, these techniques are either not personal (that is, consider only tweets in the timeline of a specific user) or are too expensive to be realized on a mobile device. Given that 80% of active Twitter users access the site on mobile devices, in this article we present a lightweight, personal, on-demand, topic modeling-based tweets summarization engine called TOTEM, designed for such devices. Specifically, TOTEM first preprocesses recent tweets in a user’s timeline and exploits Latent Dirichlet Allocation-based topic modeling to assign each preprocessed tweet to a topic. Then it generates a ranked list of relevant tweets, a topic label, and a topic summary for each of the topics. Our experimental study with real-world data sets demonstrates the superiority of TOTEM.