Discovering Newsworthy Themes from Sequenced Data: A Step Towards Computational Journalism
Qi Fan, Yuchen Li, Dongxiang Zhang, Kian‐Lee Tan · 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$k$-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.