Discovering Newsworthy Themes from Sequenced Data: A Step Towards Computational Journalism

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 <inline-formula> <tex-math notation="LaTeX">k</tex-math> <inline-g...

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Bibliographic Details
Published in:IEEE transactions on knowledge and data engineering Vol. 29; no. 7; pp. 1398 - 1411
Main Authors: Fan, Qi, Li, Yuchen, Zhang, Dongxiang, Tan, Kian-Lee
Format: Journal Article
Language:English
Published: New York IEEE 01.07.2017
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithm design and analysis
Algorithms
approximate algorithms
Approximation
Approximation algorithms
Automation
Computational efficiency
Computational journalism
Computing time
Datasets
Electronic commerce
Electronic mail
Engineering profession
Games
History
News
news theme discovery
Pruning
Query processing
Run time (computers)
Running
sequenced data
Summaries
ISSN:1041-4347, 1558-2191
Online Access:Get full text
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Summary: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 <inline-formula> <tex-math notation="LaTeX">k</tex-math> <inline-graphic xlink:href="fan-ieq1-2685587.gif"/> </inline-formula>-Sketch query that aims to find <inline-formula><tex-math notation="LaTeX"> k</tex-math> <inline-graphic xlink:href="fan-ieq2-2685587.gif"/> </inline-formula> 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 <inline-formula><tex-math notation="LaTeX"> k</tex-math> <inline-graphic xlink:href="fan-ieq3-2685587.gif"/> </inline-formula>-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 <inline-formula><tex-math notation="LaTeX">k</tex-math> <inline-graphic xlink:href="fan-ieq4-2685587.gif"/> </inline-formula> 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.
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ISSN:1041-4347
1558-2191
DOI:10.1109/TKDE.2017.2685587