TEXT GENERATION: TECHNIQUES, EVOLUTION AND CREATIVE APPLICATIONS    

Authors : Mohd Hyder Gouri; Mohd Nafees

Publishing Date : 2023

DOI : https://doi.org/10.52458/9789388996747.nsp2023.eb.ch-09

ISBN : 978-93-88996-92-1

Pages : 72

Chapter id : GU/NSP/EB/EFMLDSP/2023/Ch-09

Abstract : Text generation is a fascinating field within natural language processing, containing a wide range of strategies for synthesizing textual information. This chapter analyzes the evolution of text creation, from rule-based systems to cutting-edge transformer models. It goes into rule-based text production, machine learning-based techniques, and the important function of transformer models. Additionally, it examines the application of text generation in creative writing, including poetry, storytelling, and art. This chapter emphasizes the adaptability and promise of text generation, bridging the gap between technology and human creativity.

Keywords : Text creation, rule-based, machine learning, transformers, creative writing, NLP, GPT, BERT, AI, poetry generation, narrative.

Cite : Gouri, M. H., & Nafees, M. (2023). Text Generation: Techniques, Evolution And Creative Applications (1st ed., p. 72). Noble Science Press. https://doi.org/10.52458/9789388996747.nsp2023.eb.ch-09

References :
  1. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., ... & Polosukhin, I. (2017). Attention is all you need. In Advances in neural information processing systems (pp. 30-31).
  2. Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., & Sutskever, I. (2019). Language models are unsupervised multitask learners. OpenAI, 1(8), 9.
  3. Devlin, J., Chang, M. W., Lee, K., & Toutanova, K. (2018). BERT: Bidirectional Encoder Representations from Transformers. arXiv preprint arXiv:1810.04805.
  4. Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., ... & Bengio, Y. (2014). Generative adversarial nets. In Advances in neural information processing systems (pp. 2672-2680).
  5. Al-Zewairi, M., Almajali, S., & Ayyash, M. (2020). Unknown security attack detection using shallow and deep ANN classifiers. Electronics, 9(12), 2006.
  6. Gupta, V. (2011). Recent trends in text classification techniques. International journal of computer applications, 35(6), 45-51.
  7. Choetkiertikul, M., Dam, H. K., Tran, T., Pham, T., Ragkhitwetsagul, C., & Ghose, A. (2021). Automatically recommending components for issue reports using deep learning. Empirical Software Engineering, 26, 1-39.