[Objective] Mapping the biomedical text to the super thesaurus in the biomedical field to obtain the biomedical terms contained in the text and their corresponding concepts, and integrate the terms and concepts as background knowledge into the text summary model to improve the text summary model in biomedicine The quality of the summary generation on the text.

[Methods] This method first obtains the important content of the text through extractive abstract technology, and then combines the important content of the text with the knowledge base in the biomedical field to extract the terms contained in the important content of the text and its corresponding knowledge base concept, and integrate it into the neural network generative abstract as background knowledge In the attention mechanism of the model, the model can not only focus on the important information inside the text under the guidance of domain knowledge, but also suppress the noise problems that may occur due to the introduction of external information, and significantly improve the quality of abstract generation.

[Results] The experimental results on three biomedical field data sets verify the effectiveness of the proposed method. The average ROUGE of the proposed model PG-meta on the three data sets reaches 31.06, which is 1.51 higher than the average ROUGE of the original PG model.

[Limitations] The impact of different ways of acquiring background knowledge in biomedical fields on the effectiveness of model enhancement remains to be further explored.

[Conclusions] The proposed method can help the model better learn the deep meaning of biomedical texts and improve the quality of abstract generation.