A Text Summarization Model Based on Dual Pointer Network Fused with Keywords

Xingke Du, Ning Ouyang, Xiaodong Cai

2024

Abstract

In text summarization tasks, the importance of keywords in the text is often overlooked, resulting in the generated summary deviating from the original meaning of the text. To address this issue, a text summarization generation model GMDPK (Generation Model based on Dual Pointer network fused with Keywords) is proposed. Firstly, we design a keyword extraction module MTT (Module based on Topic Awareness and Title Orientation). It enriches semantic features by mining potential themes, and uses highly summarized and valuable information in the title to guide keyword generation, resulting in a summary that is closer to the original meaning of the text. In addition, we add an ERNIE pretraining language model in the word embedding layer to enhance the representation of Chinese text syntax structure and entity phrases. Finally, a keyword information pointer is added to the original single pointer generation network, forming a dual pointer network. This helps to improve the coverage of the copying mechanism and more effectively mine keyword information. Experiments were conducted on the Chinese dataset LCSTS, and the results showed that compared to other existing models, the summary generated by GMDPK can contain more key information, with higher accuracy and better readability.

Paper Citation

in Harvard Style

Du X., Ouyang N. and Cai X. (2024). A Text Summarization Model Based on Dual Pointer Network Fused with Keywords. In Proceedings of the 1st International Conference on Data Mining, E-Learning, and Information Systems - Volume 1: DMEIS; ISBN 978-989-758-715-3, SciTePress, pages 82-86. DOI: 10.5220/0012881900004536

in Bibtex Style

@conference{dmeis24,
author={Xingke Du and Ning Ouyang and Xiaodong Cai},
title={A Text Summarization Model Based on Dual Pointer Network Fused with Keywords},
booktitle={Proceedings of the 1st International Conference on Data Mining, E-Learning, and Information Systems - Volume 1: DMEIS},
year={2024},
pages={82-86},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012881900004536},
isbn={978-989-758-715-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Data Mining, E-Learning, and Information Systems - Volume 1: DMEIS
TI - A Text Summarization Model Based on Dual Pointer Network Fused with Keywords
SN - 978-989-758-715-3
AU - Du X.
AU - Ouyang N.
AU - Cai X.
PY - 2024
SP - 82
EP - 86
DO - 10.5220/0012881900004536
PB - SciTePress