PNFS: Personalized web news filtering and summarization

Information on the World Wide Web is congested with large amounts of news contents. Recommending, filtering, and summarization of Web news have become hot topics of research in Web intelligence, aiming to find interesting news for users and give concise content for reading. This paper presents 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. Two Web news recommendation methods are proposed to keep tracking news and find topic interesting news for users. A keyword knowledge base is maintained and provides real-time updates to reflect the news topic information and the user's interest preferences. The non-news content irrelevant to the news Web page is filtered out. A keyword extraction method based on lexical chains is proposed that uses the semantic similarity and the relatedness degree to represent the semantic relations between words. Word sense disambiguation is also performed in the built lexical chains. Experiments on Web news pages and journal articles show that the proposed keyword extraction method is effective. An example run of our PNFS system demonstrates the superiority of this Web intelligence system.