A Model to Personalized Text Summarization Generation Based on Eye Tracking

被引：0

作者：

Jie Z. ^{[1
]}

Ye Y. ^{[1
]}

Cheng S. ^{[1
]}

机构：

[1] School of Computer Science and Technology, Zhejiang University of Technology, Hangzhou

来源：

Jisuanji Fuzhu Sheji Yu Tuxingxue Xuebao/Journal of Computer-Aided Design and Computer Graphics | 2023年 / 35卷 / 10期

关键词：

eye-tracking; human-computer interaction; natural language processing; text summarization;

D O I：

10.3724/SP.J.1089.2023.19666

中图分类号：

学科分类号：

摘要：

In order to meet the personalized demands of various users for summarization generation, a gaze-based key information guide network (GKGN) is proposed. Firstly, it uses eye-movement data, such as fixation and saccade in the reading process, to extract key eye-movement information. The key eye-movement information is encoded by long-short term memory (LSTM) networks. Secondly, the hidden layer state of the key eye-movement information is fused with mechanisms of attention. Apart from that, intra-attention model and pointer generator network (PGN) are fused to generate the personalized text summarization. The ADEGBTS dataset, which contains users’ eye-movement data on reading Chinese news, is used for evaluating the GKGN model. The results show that, compared with existing text summarization models, the GKGN model scored 48.68% higher on the ROUGE. In addition, a text summarization system is designed and developed, and the user test results show that the system can efficiently generate the personalized summarization. © 2023 Institute of Computing Technology. All rights reserved.

引用

页码：1620 / 1628

页数：8

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