Journal: International Joint Conference on Natural Language Processing
Languages: English, French
Programming languages: Python
Project website: https://github.com/adrien-bougouin/KeyBench/tree/ijcnlp_2013
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 topranked topics.