Novelty and redundancy detection in adaptive filtering
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
Retrieval and novelty detection at the sentence level
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
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News videos delivered from different sources constitute a huge volume of daily information. These videos, overall, form a huge collection of news stories that are intertwined with various novel and old topic themes. To date, it remains a challenging task on how to automatically extract a concise view of news stories according to topic themes. This doctoral thesis studies the issues in story dependency threading and topical auto-documentary in news stories. Initially, a co-clustering algorithm is proposed to perform the news story clustering by exploiting the duality between stories and multi-modal concepts. Then, the novelty and redundancy detection is performed to capture the relationship among stories of a topic. To facilitate the fast navigation of news topic, a novel topic structure is then proposed to chains the dependencies of stories. A main thread is extracted to highlight the important aspects of a theme. A news video editing optimization algorithm can be directly applied to automatically select suitable video and speech contents from the original video source to create an edited video documentary.