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Information on the World Wide Web is congested with large amounts of news contents. Recommendation, filtering, and summarization of Web news have received much attention in Web intelligence, aiming to find interesting news and summarize concise content for users. In this paper, we present our research on developing the Personalized News Filtering and Summarization system (PNFS). An embedded learning component of PNFS induces a user interest model and recommends personalized news. A keyworddoi:10.1109/ictai.2011.68 dblp:conf/ictai/WuXWD11 fatcat:qepzzmiz6re3joccc5asp7di7m