Discovering Newsworthy Themes From Sequenced Data: A Step Towards Computational Journalism - 2017 PROJECT TITLE : Discovering Newsworthy Themes From Sequenced Data: A Step Towards Computational Journalism - 2017 ABSTRACT: Automatic discovery of newsworthy themes from sequenced knowledge will relieve journalists from manually poring over a large quantity of information in order to seek out fascinating news. During this paper, we tend to propose a unique k -Sketch query that aims to search out k placing streaks to best summarize a topic. Our scoring perform takes into consideration streak strikingness and streak coverage at the identical time. We study the k -Sketch question processing in each offline and on-line eventualities, and propose numerous streak-level pruning techniques to search out hanging candidates. Among those candidates, we tend to then develop approximate strategies to discover the k most representative streaks with theoretical bounds. We conduct experiments on four real datasets, and also the results demonstrate the potency 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. Did you like this research project? To get this research project Guidelines, Training and Code... Click Here facebook twitter google+ linkedin stumble pinterest Engagement dynamics and sensitivity analysis of YouTube videos - 2017 On Spectral Analysis of Signed and Dispute Graphs: Application to Community Structure - 2017