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From Statistical Methods to Deep Learning, Automatic Keyphrase Prediction: A Survey

Computation and Language 2023-05-05 v1 Artificial Intelligence

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.

Keywords

Cite

@article{arxiv.2305.02579,
  title  = {From Statistical Methods to Deep Learning, Automatic Keyphrase Prediction: A Survey},
  author = {Binbin Xie and Jia Song and Liangying Shao and Suhang Wu and Xiangpeng Wei and Baosong Yang and Huan Lin and Jun Xie and Jinsong Su},
  journal= {arXiv preprint arXiv:2305.02579},
  year   = {2023}
}

Comments

Information Processing & Management

R2 v1 2026-06-28T10:25:18.583Z