Abstract
General summary of news content is a task under the general heading of “information summarisation,” and it is recognised as a way of overcoming information overload. How does one summarise a large number of articles with their time as well as topical content? Here we introduce one technique, dynamic topic models, built using discrete non-parametric techniques, and demonstrate our software that can do this relatively efficiently using multi-core methods. Examples are used from a 760k collection of news articles from the Australian Broadcasting Commission (ABC) website over a ten year period.
Original language | English |
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Title of host publication | Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining |
Place of Publication | United States |
Publisher | Association for Computing Machinery (ACM) |
Pages | 1-5 |
Number of pages | 5 |
ISBN (Print) | 9781450329569 |
Publication status | Published - 2014 |
Event | NewsKDD 2014: Data Science for News Publishing - New York, New York, United States Duration: 24 Aug 2014 → 27 Aug 2014 https://www.kdd.org/kdd2014/workshops.html |
Conference
Conference | NewsKDD 2014 |
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Abbreviated title | NewsKDD 2014 |
Country/Territory | United States |
City | New York |
Period | 24/08/14 → 27/08/14 |
Internet address |