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Evolution of Neural Text Generation: Comparative Analysis

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Part of the Advances in Intelligent Systems and Computing book series (AISC,volume 1158)


In the past few years, various advancements have been made in Language Models owing to the formulation of various new algorithms such as Generative Adversarial Networks (GANs), ELMo and Bidirectional Encoder Representations from Transformers (BERT). Text Generation, one of the most important language modeling problems has shown great promise recently due to the advancement of more efficient and competent context-dependent algorithms such as ElMo and BERT and GPT-2 compared to preceding context independent algorithms such as word2vec and GloVe. In this paper, we compare the various attempts to Text Generation showcasing the benefits of each in their own unique form.


  • Word2vec
  • GloVe
  • ELMo
  • BERT
  • GANs
  • GPT-2

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  • DOI: 10.1007/978-981-15-4409-5_71
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Fig. 1


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Correspondence to Lakshmi Kurup .

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Kurup, L., Narvekar, M., Sarvaiya, R., Shah, A. (2021). Evolution of Neural Text Generation: Comparative Analysis. In: Bhatia, S.K., Tiwari, S., Ruidan, S., Trivedi, M.C., Mishra, K.K. (eds) Advances in Computer, Communication and Computational Sciences. Advances in Intelligent Systems and Computing, vol 1158. Springer, Singapore.

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