A Comprehensive Review of Text Generation: From NLP to Hybrid Mechanisms
Hiba Amjed, Abeer Khalid Al-Mashhadany
Asian Journal of Research in Computer Science · pp. 233–242 · Published 2 Jun 2025
10.9734/ajrcos/2025/v18i6694Abstract
The natural language processing (NLP) field is facing a significant challenge in text generation, which is considered more complex than text understanding. The rapid expansion of electronic communication between people has made research in text generation essential. Websites across different domains now aim to respond to users using natural language. This study classifies text generation based on two principles (the level of generation and the technique used). This classification offers a comprehensive view of how text generation has developed and how different methods contribute to generating coherent and contextual text. The study recognizes deep learning as the principal approach in text generation and recommends that transforming deep learning models to include self-attention mechanisms and knowledge understanding is a promising direction for future research.
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