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

arXiv: https://arxiv.org/abs/2305.02579 · HTML: https://mlsanity.com/paper/2305.02579/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 73 of its 173 references that are in the map.
- First posted on arXiv 4 May 2023.

## Builds on (most cited first, 30 of 73)

- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [Longformer: The Long-Document Transformer](https://mlsanity.com/paper/2004.05150/) (2020)
- [Deep contextualized word representations](https://mlsanity.com/paper/1802.05365/) (2018)
- [MiniLM: Deep Self-Attention Distillation for Task-Agnostic Compression of Pre-Trained Transformers](https://mlsanity.com/paper/2002.10957/) (2020)
- [SciBERT: A Pretrained Language Model for Scientific Text](https://mlsanity.com/paper/1903.10676/) (2019)
- [Distributed Representations of Sentences and Documents](https://mlsanity.com/paper/1405.4053/) (2014)
- [Unified Language Model Pre-training for Natural Language Understanding and Generation](https://mlsanity.com/paper/1905.03197/) (2019)
- [ERNIE: Enhanced Language Representation with Informative Entities](https://mlsanity.com/paper/1905.07129/) (2019)
- [Incorporating Copying Mechanism in Sequence-to-Sequence Learning](https://mlsanity.com/paper/1603.06393/) (2016)
- [RippleNet: Propagating User Preferences on the Knowledge Graph for Recommender Systems](https://mlsanity.com/paper/1803.03467/) (2018)
- [Modeling Coverage for Neural Machine Translation](https://mlsanity.com/paper/1601.04811/) (2016)
- [Unsupervised Learning of Sentence Embeddings using Compositional n-Gram Features](https://mlsanity.com/paper/1703.02507/) (2017)
- [Deep Keyphrase Generation](https://mlsanity.com/paper/1704.06879/) (2017)
- [Multi-Reward Reinforced Summarization with Saliency and Entailment](https://mlsanity.com/paper/1804.06451/) (2018)
- [One Size Does Not Fit All: Generating and Evaluating Variable Number of Keyphrases](https://mlsanity.com/paper/1810.05241/) (2018)
- [Unsupervised Keyphrase Extraction with Multipartite Graphs](https://mlsanity.com/paper/1803.08721/) (2018)
- [Keyphrase Generation with Correlation Constraints](https://mlsanity.com/paper/1808.07185/) (2018)
- [Neural Keyphrase Generation via Reinforcement Learning with Adaptive Rewards](https://mlsanity.com/paper/1906.04106/) (2019)
- [KPTimes: A Large-Scale Dataset for Keyphrase Generation on News Documents](https://mlsanity.com/paper/1911.12559/) (2019)
- [One2Set: Generating Diverse Keyphrases as a Set](https://mlsanity.com/paper/2105.11134/) (2021)
- [Exclusive Hierarchical Decoding for Deep Keyphrase Generation](https://mlsanity.com/paper/2004.08511/) (2020)
- [Semi-Supervised Learning for Neural Keyphrase Generation](https://mlsanity.com/paper/1808.06773/) (2018)
- [Title-Guided Encoding for Keyphrase Generation](https://mlsanity.com/paper/1808.08575/) (2018)
- [Learning Rich Representation of Keyphrases from Text](https://mlsanity.com/paper/2112.08547/) (2021)
- [Entity-Duet Neural Ranking: Understanding the Role of Knowledge Graph Semantics in Neural Information Retrieval](https://mlsanity.com/paper/1805.07591/) (2018)
- [Topic-Aware Neural Keyphrase Generation for Social Media Language](https://mlsanity.com/paper/1906.03889/) (2019)
- [An Integrated Approach for Keyphrase Generation via Exploring the Power of Retrieval and Extraction](https://mlsanity.com/paper/1904.03454/) (2019)
- [Open Domain Web Keyphrase Extraction Beyond Language Modeling](https://mlsanity.com/paper/1911.02671/) (2019)
- [An Empirical Study on Neural Keyphrase Generation](https://mlsanity.com/paper/2009.10229/) (2020)

## Built on by (most cited first, 14 of 14)

- [EUROPA: A Legal Multilingual Keyphrase Generation Dataset](https://mlsanity.com/paper/2403.00252/) (2024)
- [Pre-Trained Language Models for Keyphrase Prediction: A Review](https://mlsanity.com/paper/2409.01087/) (2024)
- [One2set + Large Language Model: Best Partners for Keyphrase Generation](https://mlsanity.com/paper/2410.03421/) (2024)
- [Towards Better Multi-modal Keyphrase Generation via Visual Entity Enhancement and Multi-granularity Image Noise Filtering](https://mlsanity.com/paper/2309.04734/) (2023)
- [Response Enhanced Semi-supervised Dialogue Query Generation](https://mlsanity.com/paper/2312.12713/) (2023)
- [An Improved Method for Class-specific Keyword Extraction: A Case Study in the German Business Registry](https://mlsanity.com/paper/2407.14085/) (2024)
- [Utilizing Cognitive Signals Generated during Human Reading to Enhance Keyphrase Extraction from Microblogs](https://mlsanity.com/paper/2606.26485/) (2026)
- [ConExion: Concept Extraction with Large Language Models](https://mlsanity.com/paper/2504.12915/) (2025)
- [An efficient domain-independent approach for supervised keyphrase extraction and ranking](https://mlsanity.com/paper/2404.07954/) (2024)
- [Cross-Domain Keyword Extraction with Keyness Patterns](https://mlsanity.com/paper/2409.18724/) (2024)
- [A Preliminary Survey of Semantic Descriptive Model for Images](https://mlsanity.com/paper/2501.08352/) (2025)
- [Towards an automatic method for generating topical vocabulary test forms for specific reading passages](https://mlsanity.com/paper/2505.18762/) (2025)
- [An Analysis of Datasets, Metrics and Models in Keyphrase Generation](https://mlsanity.com/paper/2506.10346/) (2025)
- [Leveraging Human Reading Behavior for Keyphrase Extraction: A Webcam-based Eye-tracking Corpus](https://mlsanity.com/paper/2608.10688/) (2026)

## Abstract (arXiv)

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.