TopicRank: Graph-Based Topic Ranking for Keyphrase Extraction

This paper presents TopicRank, a graph-based keyphrase extraction method that relies on a topical representation of the document and significantly outperforms state-of-the-art methods on three datasets.

Abstract

Keyphrase extraction is the task of iden-tifying single or multi-word expressions that represent the main topics of a doc-ument. In this paper we present Topi-cRank, a graph-based keyphrase extrac-tion method that relies on a topical rep-resentation of the document. Candidate keyphrases are clustered into topics and used as vertices in a complete graph. A graph-based ranking model is applied to assign a significance score to each topic. Keyphrases are then generated by select-ing a candidate from each of the top-ranked topics. We conducted experiments on four evaluation datasets of different languages and domains. Results show that TopicRank significantly outperforms state-of-the-art methods on three datasets. 1