From Statistical Methods to Deep Learning, Automatic Keyphrase Prediction: A Survey
This work provides a comprehensive overview and standardized comparison for researchers in natural language processing, but it is incremental as it builds upon existing surveys.
This survey paper tackles the problem of keyphrase prediction by comprehensively summarizing 167 previous works, focusing on deep learning-based methods, and conducting experiments to compare representative models using identical datasets and evaluation metrics for the first time.
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.