Experiments with Dynamic Topic Models

Jinjing Li, Wray Buntine

Research output: A Conference proceeding or a Chapter in BookConference contributionpeer-review


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 languageEnglish
Title of host publication Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining
Place of PublicationUnited States
PublisherAssociation for Computing Machinery (ACM)
Number of pages5
ISBN (Print)9781450329569
Publication statusPublished - 2014
EventNewsKDD 2014: Data Science for News Publishing - New York, New York, United States
Duration: 24 Aug 201427 Aug 2014


ConferenceNewsKDD 2014
Abbreviated titleNewsKDD 2014
Country/TerritoryUnited States
CityNew York
Internet address


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