A Position-Biased PageRank Algorithm for Keyphrase Extraction

Given the large amounts of online textual documents available these days, e. g. , news articles and scientific papers, effective methods for extracting keyphrases, which provide a highlevel topic description of a document, are greatly needed. We propose PositionRank, an unsupervised graph-based approach to keyphrase extraction that incorporates information from all positions of a word’s occurrences into a biased PageRank to extract keyphrases. Our model obtains remarkable improvements in performance over strong baselines.

Authors

• Corina Florescu
• Cornelia Caragea Computer Science, University of Illinois Chicago

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