Year: 2,013
Journal: International Joint Conference on Natural Language Processing
Languages: English, French
Programming languages: Python
Input data:

Plain text

Output data:

keywords, keyphrases

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.

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