From statistical methods to deep learning,automatic keyphrase prediction: A survey |
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| Institution: | 1. Smart Tourism Eudcation Platform (STEP), College of Hotel and Tourism Management, Kyung Hee University, South Korea;2. Department of Tourism, Hospitality and Event Management, University of Florida, USA;3. Information Systems Institute, Leipzig University, Germany;1. College of Big Data and Intelligent Engineering, Yangtze Normal University, Chongqing 408100, China;2. Chongqing Key Laboratory of Computational Intelligence, Chongqing University of Posts and Telecommunications, Chongqing 400065, China;3. College of Computer and Information Science, Southwest University, Chongqing 400715, China;1. School of Economics and Management, Xidian University, Xi''an 710126, China;2. School of Information Management, Wuhan University, Wuhan 430072, China;3. Information Retrieval and Knowledge Mining Laboratory, Wuhan University, Wuhan 430072, China |
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| Abstract: | Keyphrase prediction aims to generate phrases (keyphrases) that highly summarizes a given document. Recently, researchers have conducted in-depth studies on this task from various perspectives. In this paper, we comprehensively summarize representative studies from the perspectives of dominant models, datasets and evaluation metrics. Our work analyzes up to 167 previous works, achieving greater coverage of this task than previous surveys. Particularly, we focus highly on deep learning-based keyphrase prediction, which attracts increasing attention of this task in recent years. Afterwards, we conduct several groups of experiments to carefully compare representative models. To the best of our knowledge, our work is the first attempt to compare these models using the identical commonly-used datasets and evaluation metric, facilitating in-depth analyses of their disadvantages and advantages. Finally, we discuss the possible research directions of this task in the future. |
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| Keywords: | Keyphrase prediction Automatic keyphrase extraction Automatic keyphrase generation Deep learning |
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