Article: GAN-based reconstruction for the automatic generation of business English emails and its potential Journal: International Journal of Information and Communication Technology (IJICT) 2026 Vol.27 No.92 pp.60 - 78 Abstract: Traditional business English emails generated by artificial intelligence are often rigid and lack flexibility, making it difficult to meet complex and variable communication needs. To address this issue, this paper explores the application potential of generative adversarial networks (GANs) in this field. It designs a new conditional generative adversarial network architecture, enabling the model to learn and imitate the appropriate style and accurate content of professional business letters. Experiments on an open business email dataset show that, compared with mainstream generation models, the method proposed in this paper improves the practicality of generated emails by 15.7% in human evaluation and increases the accuracy of key terms by 8.2%. This demonstrates that generative adversarial network technology can effectively 'reconstruct' the quality and adaptability of automatically generated business emails, providing a new path for enhancing the professionalism of intelligent communication tools. Inderscience Publishers - linking academia, business and industry through research

Title: GAN-based reconstruction for the automatic generation of business English emails and its potential

Authors: Xingxin Liu; Hui Ye

Addresses: School of Education, Taylor's University, Kuala Lumpur, 47500, Malaysia ' School of Foreign Studies and Trade, Hubei University of Automotive Technology, Shiyan, 442000, China; School of Education, Taylor's University, Kuala Lumpur, 47500, Malaysia

Abstract: Traditional business English emails generated by artificial intelligence are often rigid and lack flexibility, making it difficult to meet complex and variable communication needs. To address this issue, this paper explores the application potential of generative adversarial networks (GANs) in this field. It designs a new conditional generative adversarial network architecture, enabling the model to learn and imitate the appropriate style and accurate content of professional business letters. Experiments on an open business email dataset show that, compared with mainstream generation models, the method proposed in this paper improves the practicality of generated emails by 15.7% in human evaluation and increases the accuracy of key terms by 8.2%. This demonstrates that generative adversarial network technology can effectively 'reconstruct' the quality and adaptability of automatically generated business emails, providing a new path for enhancing the professionalism of intelligent communication tools.

Keywords: generative adversarial network; GAN; business English email; natural language generation; style control.

DOI: 10.1504/IJICT.2026.155941

International Journal of Information and Communication Technology, 2026 Vol.27 No.92, pp.60 - 78

Received: 15 Feb 2026
Accepted: 30 Mar 2026
Published online: 26 Aug 2026 *

Title: GAN-based reconstruction for the automatic generation of business English emails and its potential

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