It is a tedious task to summarize the diagnostic impressions from the image description of vascular ultrasound, and it is more possible to make mistakes because it requires rich experience and clear logical thinking ability of ultrasound radiologists. In this paper, we explore the use of a LSTM-RNN-Attention (LRA) model to automatically generate diagnostic impressions based on the description of vascular ultrasound images, and evaluate the performance of the model by ROUGE scores. Also, the paper compares this model with pointer generation network (PGN) and basic encoder-decoder (ED) model. The LRA Model achieved a ROUGE-1 score of 26.353, a ROUGE-2 score of 11.587, a ROUGE-L score of 26.168, each score was almost 8 times higher than it of (PGN). The results showed that LRA model presented better performance than these most commonly used models in the field of chinses radiological text summarization and is useful in clinical practice.