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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Veröffentlicht in:IEEE transactions on knowledge and data engineering Jg. 29; H. 7; S. 1398 - 1411
Hauptverfasser: Fan, Qi, Li, Yuchen, Zhang, Dongxiang, Tan, Kian-Lee
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 01.07.2017
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
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
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Abstract 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.
AbstractList 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.
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 [Formula Omitted]-Sketch query that aims to find [Formula Omitted] 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 [Formula Omitted]-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 [Formula Omitted] 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.
Author Zhang, Dongxiang
Fan, Qi
Li, Yuchen
Tan, Kian-Lee
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crossref_primary_10_1109_TKDE_2018_2854182
crossref_primary_10_1016_j_ins_2018_04_031
Cites_doi 10.1145/2213556.2213580
10.1023/A:1009796218281
10.1007/s10618-011-0232-z
10.1145/780555.780558
10.1007/BF01588971
10.1145/860435.860495
10.1145/2723372.2749451
10.1145/2808194.2809474
10.1137/1.9781611972795.60
10.1109/ICDE.2014.6816644
10.1145/2339530.2339762
10.1007/978-3-642-40328-6_9
10.1145/1281192.1281238
10.3141/1811-08
10.14778/1687627.1687693
10.14778/2733004.2733029
10.1007/s11280-014-0295-z
10.1007/978-3-642-13657-3_34
10.1109/TKDE.2013.44
10.1007/978-3-540-77974-2
10.1145/2601439
10.1145/290941.290954
10.1145/237814.238000
10.1145/1498759.1498766
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References ref13
ref12
ref15
ref14
ref11
ref10
berg (ref17) 2008
ref2
ref1
ref16
ref19
ref18
ref23
ref25
ref20
lichman (ref24) 2013
ref22
ref21
ref8
ref7
ref9
ref4
ref3
ref6
ref5
References_xml – ident: ref10
  doi: 10.1145/2213556.2213580
– ident: ref5
  doi: 10.1023/A:1009796218281
– ident: ref7
  doi: 10.1007/s10618-011-0232-z
– ident: ref21
  doi: 10.1145/780555.780558
– ident: ref20
  doi: 10.1007/BF01588971
– ident: ref14
  doi: 10.1145/860435.860495
– ident: ref12
  doi: 10.1145/2723372.2749451
– ident: ref16
  doi: 10.1145/2808194.2809474
– ident: ref23
  doi: 10.1137/1.9781611972795.60
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  doi: 10.1109/ICDE.2014.6816644
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  doi: 10.1145/2339530.2339762
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  doi: 10.1007/978-3-642-40328-6_9
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  doi: 10.1145/1281192.1281238
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  doi: 10.3141/1811-08
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  doi: 10.14778/1687627.1687693
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  doi: 10.14778/2733004.2733029
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  doi: 10.1007/s11280-014-0295-z
– ident: ref6
  doi: 10.1007/978-3-642-13657-3_34
– ident: ref11
  doi: 10.1109/TKDE.2013.44
– year: 2008
  ident: ref17
  publication-title: Computational Geometry Algorithms and Applications
  doi: 10.1007/978-3-540-77974-2
– ident: ref4
  doi: 10.1145/2601439
– year: 2013
  ident: ref24
  article-title: UCI machine learning repository
– ident: ref13
  doi: 10.1145/290941.290954
– ident: ref22
  doi: 10.1145/237814.238000
– ident: ref9
  doi: 10.1145/1498759.1498766
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SubjectTerms 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
Title Discovering Newsworthy Themes from Sequenced Data: A Step Towards Computational Journalism
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