GSPT-CVAE: A New Controlled Long Text Generation Method Based on T-CVAE

Aiming at the problems of incomplete characterization of text relations, poor guidance of potential representations, and low quality of model generation in the field of controllable long text generation, this paper proposes a new GSPT-CVAE model (Graph Structured Processing, Single Vector, and Potential Attention Computing Transformer-Based Conditioned Variational Autoencoder model). The model obtains a more comprehensive representation of textual relations by graph-structured processing of the input text, and at the same time obtains a single vector representation by weighted merging of the vector sequences after graph-structured processing to get an effective potential representation. In the process of potential representation guiding text generation, the model adopts a combination of traditional embedding and potential attention calculation to give full play to the guiding role of potential representation for generating text, to improve the controllability and effectiveness of text generation. The experimental results show that the model has excellent representation learning ability and can learn rich and useful textual relationship representations. The model also achieves satisfactory results in the effectiveness and controllability of text generation and can generate long texts that match the given constraints. The ROUGE-1 F1 score of this model is 0.243, the ROUGE-2 F1 score is 0.041, the ROUGE-L F1 score is 0.22, and the PPL-Word score is 34.303, which gives the GSPT-CVAE model a certain advantage over the baseline model. Meanwhile, this paper compares this model with the state-of-the-art generative models T5, GPT-4, Llama2, and so on, and the experimental results show that the GSPT-CVAE model has a certain competitiveness.