Leveraging Human Reading Behavior for Keyphrase Extraction: A Webcam-based Eye-tracking Corpus

2026-08-11

AI Summary

This study addresses the limitation of existing keyword extraction methods, which often overlook readers’ attention signals and thus fail to effectively model human reading behavior. To bridge this gap, the authors propose a low-cost eye-tracking solution using standard webcams and construct CLIS-ET, the first Chinese eye-tracking corpus in the library and information science domain, annotated with character-level eye movement features including first fixation duration (FFD), fixation count (FN), and total fixation duration (TFD). Leveraging the SearchGazer framework, they develop a data collection platform and integrate these eye-tracking features into an Att-BiLSTM+CRF model for keyword extraction. Experimental results demonstrate that incorporating eye movement information significantly enhances extraction performance, with the combination of FN and TFD yielding the best results.

Abstract

Purpose: Keyphrases are statistically and semantically important textual units that can also attract readers' attention during comprehension. However, existing keyphrase extraction (KPE) studies mainly focus on improving textual representation while largely overlooking human reading behavior. This study examines whether lightweight webcam-based eye-tracking features can improve KPE from Chinese academic abstracts in Library and Information Science (LIS). Methodology: To address the limited availability of eye-tracking data for Chinese academic reading, we developed a lightweight webcam-based data collection platform using the open-source SearchGazer library and constructed the Chinese LIS Eye-Tracking Corpus (CLIS-ET). Three character-level eye-tracking features, first fixation duration (FFD), fixation number (FN), and total fixation duration (TFD), were incorporated into KPE models to evaluate their effects on extraction performance. Findings: Eye-tracking features consistently improved KPE performance. The combination of FN and TFD achieved the best results on the Att-BiLSTM+CRF model, indicating that readers' fixation behavior provides useful signals for identifying keyphrases in academic abstracts. Originality/value: This study introduces a cost-effective webcam-based eye-tracking approach for KPE and presents CLIS-ET, a Chinese academic eye-tracking corpus containing FFD, FN, and TFD features. The results demonstrate the value of incorporating human reading behavior into keyphrase extraction. Dataset and code: https://github.com/yan-xinyi/ET_AKE and https://github.com/yan-xinyi/Reading_ET_System.

Problem

• keyphrase extraction
• human reading behavior
• eye-tracking
• Chinese academic abstracts
• Library and Information Science

Innovation

• webcam-based eye-tracking
• keyphrase extraction
• human reading behavior
• Chinese academic corpus
• fixation features