Abstract
There is an increasing need for online news aggregation and visualization. Commercial systems, such as Google News and Ask.com, have successfully launched a portal aiming at providing an aggregated view of the top news events at a given time. However, these systems, as well as previous research projects, lack the ability to personalize events according to the user's need. Furthermore, users increasingly prefer to see multiple types of media to be presented when they follow a particular event of interest. In this paper, we describe a novel framework to allow the aggregation of online sources for text articles, images, videos and TV news into news stories, while the visualization enables the users to browse and select the news events based on semantic information. The experimental results have indicated some promising results
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