A ranking approach to keyphrase extraction

This paper addresses the issue of automatically extracting keyphrases from document. Previously, this problem was formalized as classification and learning methods for classification were utilized. This paper points out that it is more essential to cast the keyphrase extraction problem as ranking and employ a learning to rank method to perform the task. As example, it employs Ranking SVM, a state-of-art method of learning to rank, in keyphrase extraction. Experiments conducted on three datasets show that Ranking SVM significantly outperforms the baseline methods of classification, indicating that it is better to exploit learning to rank techniques in keyphrase extraction. Key words: Keyphrase extraction, Learning to rank, Ranking SVM

• Type: Conference
• Year: 2009
• Where: SIGIR
• Authors: Xin Jiang, Yunhua Hu, Hang Li
• Keywords: Information Retrieval, Keyphrase Extraction, Keyphrase Extraction Problem, Ranking SVM, SIGIR 2009