# Enhancing Keyphrase Extraction from Academic Articles Using Section Structure Information

arXiv: https://arxiv.org/abs/2505.14149 · HTML: https://mlsanity.com/paper/2505.14149/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 70 references that are in the map.
- First posted on arXiv 20 May 2025.

## Builds on (most cited first, 16 of 16)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Convolutional Neural Networks for Sentence Classification](https://mlsanity.com/paper/1408.5882/) (2014)
- [ChatIE: Zero-Shot Information Extraction via Chatting with ChatGPT](https://mlsanity.com/paper/2302.10205/) (2023)
- [Every Document Owns Its Structure: Inductive Text Classification via Graph Neural Networks](https://mlsanity.com/paper/2004.13826/) (2020)
- [Growth rates of modern science: A bibliometric analysis based on the number of publications and cited references](https://mlsanity.com/paper/1402.4578/) (2014)
- [Simple Unsupervised Keyphrase Extraction using Sentence Embeddings](https://mlsanity.com/paper/1801.04470/) (2018)
- [Hi-Transformer: Hierarchical Interactive Transformer for Efficient and Effective Long Document Modeling](https://mlsanity.com/paper/2106.01040/) (2021)
- [Open Domain Web Keyphrase Extraction Beyond Language Modeling](https://mlsanity.com/paper/1911.02671/) (2019)
- [Importance Estimation from Multiple Perspectives for Keyphrase Extraction](https://mlsanity.com/paper/2110.09749/) (2021)
- [Introducing Syntactic Structures into Target Opinion Word Extraction with Deep Learning](https://mlsanity.com/paper/2010.13378/) (2020)
- [Keyphrase Extraction from Disaster-related Tweets](https://mlsanity.com/paper/1910.07897/) (2019)
- [Enhancing Identification of Structure Function of Academic Articles Using Contextual Information](https://mlsanity.com/paper/2111.14110/) (2021)
- [Enhancing Keyphrase Extraction from Academic Articles with their Reference Information](https://mlsanity.com/paper/2111.14106/) (2021)
- [Keyword Extraction from Short Texts with a Text-To-Text Transfer Transformer](https://mlsanity.com/paper/2209.14008/) (2022)
- [Duration modeling with semi-Markov Conditional Random Fields for keyphrase extraction](https://mlsanity.com/paper/2209.09149/) (2022)
- [Feature engineering vs. deep learning for paper section identification: Toward applications in Chinese medical literature](https://mlsanity.com/paper/2412.11125/) (2024)

## Built on by (most cited first, 2 of 2)

- [Enhancing Unsupervised Keyword Extraction in Academic Papers through Integrating Highlights with Abstract](https://mlsanity.com/paper/2604.19505/) (2026)
- [Which Sections of a Research Paper Best Reveal Its Research Methods? Evidence from Library and Information Science](https://mlsanity.com/paper/2606.19051/) (2026)

## Abstract (arXiv)

The exponential increase in academic papers has significantly increased the time required for researchers to access relevant literature. Keyphrase Extraction (KPE) offers a solution to this situation by enabling researchers to efficiently retrieve relevant literature. The current study on KPE from academic articles aims to improve the performance of extraction models through innovative approaches using Title and Abstract as input corpora. However, the semantic richness of keywords is significantly constrained by the length of the abstract. While full-text-based KPE can address this issue, it simultaneously introduces noise, which significantly diminishes KPE performance. To address this issue, this paper utilized the structural features and section texts obtained from the section structure information of academic articles to extract keyphrase from academic papers. The approach consists of two main parts: (1) exploring the effect of seven structural features on KPE models, and (2) integrating the extraction results from all section texts used as input corpora for KPE models via a keyphrase integration algorithm to obtain the keyphrase integration result. Furthermore, this paper also examined the effect of the classification quality of section structure on the KPE performance. The results show that incorporating structural features improves KPE performance, though different features have varying effects on model efficacy. The keyphrase integration approach yields the best performance, and the classification quality of section structure can affect KPE performance. These findings indicate that using the section structure information of academic articles contributes to effective KPE from academic articles. The code and dataset supporting this study are available at https://github.com/yan-xinyi/SSB_KPE.