Document-Specific Keyphrase Extraction Using Sequential Patterns with Wildcards

Abstract

Finding good keyphrases for a document is beneficial for many applications, such as text summarization, browsing, and indexing. In this paper, we propose a sequential pattern mining based document-specific keyphrase extraction method. Our key innovation is to use wildcards (or gap constraints) to help extract sequential patterns, where the flexible wildcard constraints within a pattern can capture semantic relationships between words. To achieve this goal, we regard each single document as a sequential dataset, and propose an efficient algorithm to mine sequential patterns with wildcard and one-off conditions that allows important keyphrases to be captured during the mining process. For each extracted keyphrase candidate, we use some statistical pattern features to characterize it. A supervised learning classifier is trained to identify keyphrases from a test document. Comparisons on keyphrase benchmark datasets confirm that our document-specific keyphrase extraction method is effective in improving the quality of extracted keyphrases.