# Enhancing Keyphrase Extraction from Academic Articles with their Reference Information

arXiv: https://arxiv.org/abs/2111.14106 · HTML: https://mlsanity.com/paper/2111.14106/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 47 references that are in the map.
- First posted on arXiv 28 Nov 2021.

## Builds on (most cited first, 6 of 6)

- [Deep Keyphrase Generation](https://mlsanity.com/paper/1704.06879/) (2017)
- [Neural Keyphrase Generation via Reinforcement Learning with Adaptive Rewards](https://mlsanity.com/paper/1906.04106/) (2019)
- [A Review of Keyphrase Extraction](https://mlsanity.com/paper/1905.05044/) (2019)
- [Topic-Aware Neural Keyphrase Generation for Social Media Language](https://mlsanity.com/paper/1906.03889/) (2019)
- [Keyphrase Generation: A Multi-Aspect Survey](https://mlsanity.com/paper/1910.05059/) (2019)
- [Enhancing Keyphrase Extraction from Microblogs using Human Reading Time](https://mlsanity.com/paper/2010.09934/) (2020)

## Built on by (most cited first, 6 of 6)

- [Detecting Emerging Technologies and their Evolution using Deep Learning and Weak Signal Analysis](https://mlsanity.com/paper/2205.05449/) (2022)
- [Pre-Trained Language Models for Keyphrase Prediction: A Review](https://mlsanity.com/paper/2409.01087/) (2024)
- [Enhancing Keyphrase Extraction from Academic Articles Using Section Structure Information](https://mlsanity.com/paper/2505.14149/) (2025)
- [Does Attention Mechanism Possess the Feature of Human Reading? A Perspective of Sentiment Classification Task](https://mlsanity.com/paper/2209.03557/) (2022)
- [Enhancing Unsupervised Keyword Extraction in Academic Papers through Integrating Highlights with Abstract](https://mlsanity.com/paper/2604.19505/) (2026)
- [Building a Multimodal Dataset of Academic Paper for Keyword Extraction](https://mlsanity.com/paper/2606.31069/) (2026)

## Abstract (arXiv)

With the development of Internet technology, the phenomenon of information overload is becoming more and more obvious. It takes a lot of time for users to obtain the information they need. However, keyphrases that summarize document information highly are helpful for users to quickly obtain and understand documents. For academic resources, most existing studies extract keyphrases through the title and abstract of papers. We find that title information in references also contains author-assigned keyphrases. Therefore, this article uses reference information and applies two typical methods of unsupervised extraction methods (TF*IDF and TextRank), two representative traditional supervised learning algorithms (Na"ive Bayes and Conditional Random Field) and a supervised deep learning model (BiLSTM-CRF), to analyze the specific performance of reference information on keyphrase extraction. It is expected to improve the quality of keyphrase recognition from the perspective of expanding the source text. The experimental results show that reference information can increase precision, recall, and F1 of automatic keyphrase extraction to a certain extent. This indicates the usefulness of reference information on keyphrase extraction of academic papers and provides a new idea for the following research on automatic keyphrase extraction.