Discovering Newsworthy Themes from Sequenced Data: A Step Towards Computational Journalism - Fan, Qi; Li, Yuchen; Zhang, Dongxiang; Tan, Kian-Lee (IEEE transactions on knowledge and data engineering, 2017)
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 <inline-graphic xlink:href="fan-ieq1-2685587.gif"/> -Sketch query that aims to find k <inline-graphic xlink:href="fan-ieq2-2685587.gif"/> 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 <inline-graphic xlink:href="fan-ieq3-2685587.gif"/> -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 <inline-graphic xlink:href="fan-ieq4-2685587.gif"/> 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.