In supervised approaches for keyphrase extraction, a candidate phrase is encoded with a set of hand-crafted features and machine learning algorithms are trained to discriminate keyphrases from non-keyphrases. Although the manually-designed features have shown to work well in practice, feature engineering is a difficult process that requires expert knowledge and normally does not generalize well. In this paper, we present SurfKE, a feature learning framework that exploits the text itself to automatically discover patterns that keyphrases exhibit. Our model represents the document as a graph and automatically learns feature representation of phrases. The proposed model obtains remarkable improvements in performance over strong baselines.

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 high-level 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.

The large and growing amounts of online scholarly data present both challenges and opportunities to enhance knowledge discovery.One such challenge is to automatically extract a small set of keyphrases from a document that can accurately describe the document's content and can facilitate fast information processing.In this paper, we propose PositionRank, an unsupervised model for keyphrase extraction from scholarly documents that incorporates information from all positions of a word's occurrences into a biased PageRank.Our model obtains remarkable improvements in performance over PageRank models that do not take into account word positions as well as over strong baselines for this task.Specifically, on several datasets of research papers, PositionRank achieves improvements as high as 29.09%.