  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.
