Discovering newsworthy themes from sequenced data: A step towards computational journalism

• Main Authors: FAN, Qi, LI, Yuchen, ZHANG, Dongxiang, TAN, Kian-Lee Tan
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2017
• Subjects: Computational journalism; news theme discovery; sequenced data; approximate algorithms; Databases and Information Systems; Data Storage Systems
• Online Access: https://ink.library.smu.edu.sg/sis_research/3996; https://ink.library.smu.edu.sg/context/sis_research/article/4998/viewcontent/07883865__1_.pdf
• Institution: Singapore Management University

Automatic discovery of newsworthy themes from sequenced data can relieve journalists from manually poring over a large amount of data in order to find interesting news. In this paper, we propose a novel k -Sketch query that aims to find k striking streaks to best summarize a subject. Our scoring function takes into account streak strikingness and streak coverage at the same time. We study the k -Sketch query processing in both offline and online scenarios, and propose various streak-level pruning techniques to find striking candidates. Among those candidates, we then develop approximate methods to discover the k most representative streaks with theoretical bounds. We conduct experiments on four real datasets, and the results demonstrate the efficiency and effectiveness of our proposed algorithms: the running time achieves up to 500 times speedup and the quality of the generated summaries is endorsed by the anonymous users from Amazon Mechanical Turk.