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

arXiv: https://arxiv.org/abs/2608.10688 · HTML: https://mlsanity.com/paper/2608.10688/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 58 references that are in the map.
- First posted on arXiv 11 Aug 2026.

## Builds on (most cited first, 15 of 15)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Improving sentence compression by learning to predict gaze](https://mlsanity.com/paper/1604.03357/) (2016)
- [Leveraging Cognitive Features for Sentiment Analysis](https://mlsanity.com/paper/1701.05581/) (2017)
- [Advancing NLP with Cognitive Language Processing Signals](https://mlsanity.com/paper/1904.02682/) (2019)
- [TNT-KID: Transformer-based Neural Tagger for Keyword Identification](https://mlsanity.com/paper/2003.09166/) (2020)
- [Pre-Training with Whole Word Masking for Chinese BERT](https://mlsanity.com/paper/1906.08101/) (2019)
- [From Statistical Methods to Deep Learning, Automatic Keyphrase Prediction: A Survey](https://mlsanity.com/paper/2305.02579/) (2023)
- [Enhancing Keyphrase Extraction from Microblogs using Human Reading Time](https://mlsanity.com/paper/2010.09934/) (2020)
- [sCAKE: Semantic Connectivity Aware Keyword Extraction](https://mlsanity.com/paper/1811.10831/) (2018)
- [Complex Network based Supervised Keyword Extractor](https://mlsanity.com/paper/1909.12009/) (2019)
- [SEAL: Scientific Keyphrase Extraction and Classification](https://mlsanity.com/paper/2006.03292/) (2020)
- [Utilizing Cognitive Signals Generated during Human Reading to Enhance Keyphrase Extraction from Microblogs](https://mlsanity.com/paper/2606.26485/) (2026)
- [Topical Keyphrase Extraction with Hierarchical Semantic Networks](https://mlsanity.com/paper/1910.07848/) (2019)
- [Does Attention Mechanism Possess the Feature of Human Reading? A Perspective of Sentiment Classification Task](https://mlsanity.com/paper/2209.03557/) (2022)

## Abstract (arXiv)

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.