A Roberta-Seq2Seq based model for Chinese text abstractive summarization

Junjie Sun; Xia Hou

doi:10.1117/12.2637174

15 June 2022 A Roberta-Seq2Seq based model for Chinese text abstractive summarization

Junjie Sun, Xia Hou

Proceedings Volume 12285, International Conference on Advanced Algorithms and Neural Networks (AANN 2022); 1228516 (2022) https://doi.org/10.1117/12.2637174
Event: International Conference on Advanced Algorithms and Neural Networks (AANN 2022), 2022, Zhuhai, China

Abstract

In order to overcome the limitations of existing generative text summary generation algorithms in Chinese text and improve feature extraction ability of traditional deep learning models, a generative Chinese text summary generation model based on RoBERTa-Seq2Seq is proposed. The pre-training model RoBERTa is used to learn the dynamic meaning of current words in a specific context, so as to improve the semantic representation of words. Based on the Seq2Seq model, Luong Attention is used to further enhance global information. The experimental results show that our model’s ROUGE score is higher than some other traditional Seq2Seq models, which indicates that our RoBERTa-Seq2Seq based model can effectively improve the semantic representation ability of the generated summary in Chinese text and improve feature extraction ability of the traditional deep learning model.

Citation Download Citation

Junjie Sun and Xia Hou "A Roberta-Seq2Seq based model for Chinese text abstractive summarization", Proc. SPIE 12285, International Conference on Advanced Algorithms and Neural Networks (AANN 2022), 1228516 (15 June 2022); https://doi.org/10.1117/12.2637174

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available