Research on text generation model based on natural language processing

This paper discusses a new method of constructing text summary model. The Bidirectional gated cycle unit (Bi-GRU) is used to enhance the modeling ability of the whole sequence by introducing hidden states in both forward and backward directions. At the same time, based on Bi-GRU, this study further integrates the encoding structure information of attention, and integrates the text layout and implicit semantic characteristics of documents. In the coding and decoding stage of the model, Transformer structure is adopted, which avoids the limitations of traditional recurrent neural networks (RNN) and convolutional neural networks (CNN). Based on a self-attention mechanism, the Transformer model is more efficient at capturing long-distance dependencies in text, providing more powerful and flexible support for text generation tasks. Finally, the reliability of the proposed method is verified by experiments on real world generated data sets.