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Based on this viewpoint, we formulate the concept of contribution score, which indicates the amount to which a term in each page is utilized by other pages. To improve efficiency without loss of effectiveness, we exploit the expectations of both a Web page author and a Web search engine user on retrieval results, and restrict candidate terms that can contribute to other pages to a set of keywords of each page. In this paper, we propose three contribution-based models: C-Rank, PC-Rank and HC-Rank. Experimental results show that C-Rank provides the best precision among the models and is very effective for topic distillation tasks on the .GOV collection in TREC. 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