# Keyphrase Generation: A Text Summarization Struggle

arXiv: https://arxiv.org/abs/1904.00110 · HTML: https://mlsanity.com/paper/1904.00110/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Mar 2019.

## Built on by (most cited first, 12 of 12)

- [Learning Rich Representation of Keyphrases from Text](https://mlsanity.com/paper/2112.08547/) (2021)
- [Representation Learning for Resource-Constrained Keyphrase Generation](https://mlsanity.com/paper/2203.08118/) (2022)
- [Applying a Generic Sequence-to-Sequence Model for Simple and Effective Keyphrase Generation](https://mlsanity.com/paper/2201.05302/) (2022)
- [LDKP: A Dataset for Identifying Keyphrases from Long Scientific Documents](https://mlsanity.com/paper/2203.15349/) (2022)
- [Keyphrase Generation Beyond the Boundaries of Title and Abstract](https://mlsanity.com/paper/2112.06776/) (2021)
- [Applying Transformer-based Text Summarization for Keyphrase Generation](https://mlsanity.com/paper/2209.03791/) (2022)
- [Attend and select: A segment selective transformer for microblog hashtag generation](https://mlsanity.com/paper/2106.03151/) (2021)
- [Deep Keyphrase Completion](https://mlsanity.com/paper/2111.01910/) (2021)
- [Exploring Fine-tuned Generative Models for Keyphrase Selection: A Case Study for Russian](https://mlsanity.com/paper/2409.10640/) (2024)
- [Neural Keyphrase Generation: Analysis and Evaluation](https://mlsanity.com/paper/2304.13883/) (2023)
- [Cross-Domain Robustness of Transformer-based Keyphrase Generation](https://mlsanity.com/paper/2312.10700/) (2023)
- [An Analysis of Datasets, Metrics and Models in Keyphrase Generation](https://mlsanity.com/paper/2506.10346/) (2025)

## Abstract (arXiv)

Authors' keyphrases assigned to scientific articles are essential for recognizing content and topic aspects. Most of the proposed supervised and unsupervised methods for keyphrase generation are unable to produce terms that are valuable but do not appear in the text. In this paper, we explore the possibility of considering the keyphrase string as an abstractive summary of the title and the abstract. First, we collect, process and release a large dataset of scientific paper metadata that contains 2.2 million records. Then we experiment with popular text summarization neural architectures. Despite using advanced deep learning models, large quantities of data and many days of computation, our systematic evaluation on four test datasets reveals that the explored text summarization methods could not produce better keyphrases than the simpler unsupervised methods, or the existing supervised ones.