# An Empirical Study on Neural Keyphrase Generation

arXiv: https://arxiv.org/abs/2009.10229 · HTML: https://mlsanity.com/paper/2009.10229/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Sep 2020.

## Built on by (most cited first, 25 of 25)

- [A Survey of GPT-3 Family Large Language Models Including ChatGPT and GPT-4](https://mlsanity.com/paper/2310.12321/) (2023)
- [Learning Rich Representation of Keyphrases from Text](https://mlsanity.com/paper/2112.08547/) (2021)
- [MDERank: A Masked Document Embedding Rank Approach for Unsupervised Keyphrase Extraction](https://mlsanity.com/paper/2110.06651/) (2021)
- [Enhancing Taxonomy Completion with Concept Generation via Fusing Relational Representations](https://mlsanity.com/paper/2106.02974/) (2021)
- [Retrieval-Augmented Multilingual Keyphrase Generation with Retriever-Generator Iterative Training](https://mlsanity.com/paper/2205.10471/) (2022)
- [Heterogeneous Graph Neural Networks for Keyphrase Generation](https://mlsanity.com/paper/2109.04703/) (2021)
- [From Statistical Methods to Deep Learning, Automatic Keyphrase Prediction: A Survey](https://mlsanity.com/paper/2305.02579/) (2023)
- [Applying a Generic Sequence-to-Sequence Model for Simple and Effective Keyphrase Generation](https://mlsanity.com/paper/2201.05302/) (2022)
- [Generative Aspect-Based Sentiment Analysis with Contrastive Learning and Expressive Structure](https://mlsanity.com/paper/2211.07743/) (2022)
- [Pre-trained Language Models for Keyphrase Generation: A Thorough Empirical Study](https://mlsanity.com/paper/2212.10233/) (2022)
- [Rethinking Model Selection and Decoding for Keyphrase Generation with Pre-trained Sequence-to-Sequence Models](https://mlsanity.com/paper/2310.06374/) (2023)
- [Unsupervised Deep Keyphrase Generation](https://mlsanity.com/paper/2104.08729/) (2021)
- [KPDrop: Improving Absent Keyphrase Generation](https://mlsanity.com/paper/2112.01476/) (2021)
- [A Large-Scale Dataset for Biomedical Keyphrase Generation](https://mlsanity.com/paper/2211.12124/) (2022)
- [General-to-Specific Transfer Labeling for Domain Adaptable Keyphrase Generation](https://mlsanity.com/paper/2208.09606/) (2022)
- [EUROPA: A Legal Multilingual Keyphrase Generation Dataset](https://mlsanity.com/paper/2403.00252/) (2024)
- [Search-Engine-augmented Dialogue Response Generation with Cheaply Supervised Query Production](https://mlsanity.com/paper/2302.09300/) (2023)
- [SimCKP: Simple Contrastive Learning of Keyphrase Representations](https://mlsanity.com/paper/2310.08221/) (2023)
- [Unsupervised Domain Adaptation for Keyphrase Generation using Citation Contexts](https://mlsanity.com/paper/2409.13266/) (2024)
- [On Leveraging Encoder-only Pre-trained Language Models for Effective Keyphrase Generation](https://mlsanity.com/paper/2402.14052/) (2024)
- [ITTC @ TREC 2021 Clinical Trials Track](https://mlsanity.com/paper/2202.07858/) (2022)
- [Self-Compositional Data Augmentation for Scientific Keyphrase Generation](https://mlsanity.com/paper/2411.03039/) (2024)
- [Neural Keyphrase Generation: Analysis and Evaluation](https://mlsanity.com/paper/2304.13883/) (2023)
- [An Analysis of Datasets, Metrics and Models in Keyphrase Generation](https://mlsanity.com/paper/2506.10346/) (2025)
- [Evaluating the Homogeneity of Keyphrase Prediction Models](https://mlsanity.com/paper/2602.12989/) (2026)

## Abstract (arXiv)

Recent years have seen a flourishing of neural keyphrase generation (KPG) works, including the release of several large-scale datasets and a host of new models to tackle them. Model performance on KPG tasks has increased significantly with evolving deep learning research. However, there lacks a comprehensive comparison among different model designs, and a thorough investigation on related factors that may affect a KPG system's generalization performance. In this empirical study, we aim to fill this gap by providing extensive experimental results and analyzing the most crucial factors impacting the generalizability of KPG models. We hope this study can help clarify some of the uncertainties surrounding the KPG task and facilitate future research on this topic.