TopicRank: Graph-Based Topic Ranking for Keyphrase Extraction

Adrien Bougouin (1) , Florian Boudin (1) , Béatrice Daille (1)

1 LS2N - équipe TALN - Traitement Automatique du Langage Naturel

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 TopicRank, a graph-based keyphrase extraction method that relies on a topical representation 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 selecting 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.

Domaines: Traitement du texte et du document

Adrien Bougouin, Florian Boudin, Béatrice Daille. TopicRank: Graph-Based Topic Ranking for Keyphrase Extraction. International Joint Conference on Natural Language Processing (IJCNLP), Oct 2013, Nagoya, Japan. pp.543-551. ⟨hal-00917969⟩