A multi-centrality index for graph-based keyword extraction
作者：
Highlights：
• Analyses of nine centrality measures with Structural Holes used for the first time in keyword extraction.
• Centrality measures are correlated and with statistical similar performance when finding keywords.
• Proposal of the multi-centrality index (MCI) to combine the most representative measures.
• MCI achieves a high precision, recall, and F1-score with statistical significance.
• Clustering algorithms could not identify well the keyword group as the MCI approach.
摘要
•Analyses of nine centrality measures with Structural Holes used for the first time in keyword extraction.•Centrality measures are correlated and with statistical similar performance when finding keywords.•Proposal of the multi-centrality index (MCI) to combine the most representative measures.•MCI achieves a high precision, recall, and F1-score with statistical significance.•Clustering algorithms could not identify well the keyword group as the MCI approach.
论文关键词：Automatic keyword extraction,Centrality measures,Complex networks,Network science,Text mining,Text networks,Clustering
论文评审过程：Received 1 April 2019, Revised 24 May 2019, Accepted 17 June 2019, Available online 25 June 2019, Version of Record 25 June 2019.
论文官网地址：https://doi.org/10.1016/j.ipm.2019.102063
