Considering the problems that the traditional Seq2Seq model cannot accurately extract key information from texts and process words outside the word list in text summarization tasks, this study proposes a pointer generator network (PGN) model based on Fastformer. The model combines the text summarization methods of extraction and generation. Specifically, the Fastformer model is used to efficiently obtain the word embedding vector with context information, and then PGN helps choose to copy words from the source text or use vocabulary to generate new summary information, so as to solve the out-of-vocabulary (OOV) problem that often occurs in text summarization tasks. At the same time, the model uses the coverage mechanism to track the attention distribution of the past time step and dynamically adjust the importance of words to solve the problem of repeated words. Finally, the Beam Search algorithm is introduced in the decoding stage to make the decoder obtain more accurate summary results. The experiments on the dataset of auto-diagnosis dialogues provided by Auto Master in AI Studio of Baidu show that the Fastformer-PGN model proposed in this study achieves better performance in text summarization tasks of Chinese dialogues than the benchmark model.