利用人类阅读行为进行关键词抽取：基于网络摄像头的眼动语料库
Leveraging Human Reading Behavior for Keyphrase Extraction: A Webcam-based Eye-tracking Corpus
浏览论文内容
中文总结 AI 辅助
本研究构建了含FFD、FN、TFD特征的CLIS-ET语料库，将眼动特征融入KPE模型，发现FN与TFD组合在Att-BiLSTM+CRF模型上提升了中文学术摘要的关键词抽取性能。
中文摘要 AI 辅助
目的：关键词是在统计和语义层面重要的文本单元，在阅读理解过程中也能吸引读者的注意力。然而，现有的关键词抽取（KPE）研究主要聚焦于改进文本表示，却在很大程度上忽略了人类阅读行为。本研究探讨基于网络摄像头的轻量型眼动特征是否能改进图书馆与信息科学（LIS）领域中文学术摘要的关键词抽取效果。方法：为解决中文学术阅读眼动数据有限的问题，我们使用开源SearchGazer库开发了轻量型网络摄像头数据采集平台，并构建了中文LIS眼动语料库（CLIS-ET）。我们将三个字符级眼动特征——首次注视时长（FFD）、注视次数（FN）和总注视时长（TFD）——融入KPE模型，以评估它们对抽取性能的影响。结果：眼动特征持续提升了KPE性能；在Att-BiLSTM+CRF模型上，FN与TFD的组合取得了最佳效果，表明读者的注视行为为识别学术摘要中的关键词提供了有用信号。原创性/价值：本研究引入了一种用于KPE的高性价比网络摄像头眼动方法，并提供了包含FFD、FN和TFD特征的中文学术眼动语料库CLIS-ET；结果证明将人类阅读行为融入关键词抽取具有价值。数据集和代码：见此链接和此链接。
英文摘要
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.
