PTR: Phrase-Based Topical Ranking for Automatic Keyphrase Extraction in Scientific Publications

摘要

Automatic keyphrase extraction plays an important role for many information retrieval (IR) and natural language processing (NLP) tasks. Motivated by the facts that phrases have more semantic information than single words and a document consists of multiple semantic topics, we present PTR, a phrase-based topical ranking method for keyphrase extraction in scientific publications. Candidate keyphrases are divided into different topics by LDA and used as vertices in a phrase-based graph of the topic. We then decompose PageRank into multiple weighted-PageRank to rank phrases for each topic. Keyphrases are finally generated by selecting candidates according to their overall scores on all related topics. Experimental results show that PTR has good performance on several datasets.