Experiments with Dynamic Topic Models
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.