A Graph-based Approach of Automatic Keyphrase Extraction

Existing graph-based ranking techniques for keyphrase extraction only consider the connections between words in a document, ignoring the impact of the sentence. Motivated by the fact that a word must be important if it appears in many important sentences, we propose to take full advantage of the reinforcement between words andsentences by melting three kinds of relationships between them. Moreover, a document is grouped with many topics. The extracted keyphrases should be synthetic in the sense that they should deal with all the main topics in a document. Inspired by this, we take topic model into consider. Experimental results show that our approach performs betterthan state-of-the-art keyphrase extraction method on two datasets under three evaluation metrics.