Enhancing Unsupervised Keyword Extraction in Academic Papers through Integrating Highlights with Abstract

2026-04-21

AI Summary

This study investigates how to effectively leverage Highlights from academic papers to enhance unsupervised keyword extraction performance. Addressing the limitation of existing approaches that predominantly rely on abstracts, this work presents the first systematic evaluation of the complementary value of Highlights for keyword extraction and proposes an input strategy that integrates both Highlights and abstracts. Experiments conducted on datasets from computer science and library and information science, using four classic unsupervised models, demonstrate that Highlights and abstracts exhibit complementary characteristics in terms of keyword coverage and semantic content. The fusion of these two sections consistently yields significantly better performance than using either section alone, thereby substantially improving keyword extraction effectiveness.

Technology Category

• Natural Language Processing: Information Extraction
• Data Mining & Knowledge Management: Knowledge Acquisition from the Web
• Machine Learning: Learning Preferences or Rankings

Application Category

• Web Mining and Content Analysis: Normalization, clustering, classification, and summarization of Web text
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs

Abstract

Automatic keyword extraction from academic papers is a key area of interest in natural language processing and information retrieval. Although previous research has mainly focused on utilizing abstract and references for keyword extraction, this paper focuses on the highlights section - a summary describing the key findings and contributions, offering readers a quick overview of the research. Our observations indicate that highlights contain valuable keyword information that can effectively complement the abstract. To investigate the impact of incorporating highlights into unsupervised keyword extraction, we evaluate three input scenarios: using only the abstract, the highlights, and a combination of both. Experiments conducted with four unsupervised models on Computer Science (CS), Library and Information Science (LIS) datasets reveal that integrating the abstract with highlights significantly improves extraction performance. Furthermore, we examine the differences in keyword coverage and content between abstract and highlights, exploring how these variations influence extraction outcomes. The data and code are available at https://github.com/xiangyi-njust/Highlight-KPE.

Problem

• keyword extraction
• unsupervised learning
• academic papers
• highlights
• abstract

Innovation

• keyword extraction
• highlights
• unsupervised learning
• academic papers
• information fusion