TSAKE: A topical and structural automatic keyphrase extractor

Abstract

The keyphrases of a text entity are a set of words or phrases that concisely describe the main content of that text. Automatic keyphrase extraction plays an important role in natural language processing and information retrieval tasks such as text summarization, text categorization, full-text indexing, and cross-lingual text reuse. However, automatic keyphrase extraction is still a complicated task and the performance of the current keyphrase extraction methods is low. Automatic discovery of high-quality and meaningful keyphrases requires the application of useful information and suitable mining techniques. This paper proposes Topical and Structural Keyphrase Extractor (TSAKE) for the task of automatic keyphrase extraction. TSAKE combines the prior knowledge about the input langue learned by an N-gram topical model (TNG) with the co-occurrence graph of the input text to form some topical graphs. Different from most of the recent keyphrase extraction models, TSAKE uses the topic model to weight the edges instead of the nodes of the co-occurrence graph. Moreover, while TNG represents the general topics of the language, TSAKE applies network analysis techniques to each topical graph to detect finer grained sub-topics and extract more important words of each sub-topic. The use of these informative words in the ranking process of the candidate keyphrases improves the quality of the final keyphrases proposed by TSAKE. The results of our experiment studies conducted on three manually annotated datasets show the superiority of the proposed model over three baseline techniques and six state-of-the-art models.