Aiming at the problem that the abstractive summarization model in text automatic summarization task does not fully understand the context of sentence and generates duplicate contents, based on BERT (Bidirectional Encoder Representations from Transformers) and Pointer Generator Network (PGN), an abstractive summarization model for Chinese news text was proposed, namely Bidirectional Encoder Representations from Transformers-Pointer Generator Network (BERT-PGN). Firstly, combining with multi-dimensional semantic features, the BERT pre-trained language model was used to obtain the word vectors, thereby obtaining a more fine-grained text context representation. Then, through PGN model, the words were extracted from the vocabulary or the original text to form a summary. Finally, the coverage mechanism was combined to reduce the generation of duplicate contents and obtain the final summarization result. Experimental results on the single document Chinese news summary evaluation dataset of the 2017 CCF International Conference on Natural Language Processing and Chinese Computing (NLPCC2017) show that, compared with models such as PGN and Long Short-Term Memory with attention mechanism (LSTM-attention), the BERT-PGN model combined with multi-dimensional semantic features has a better understanding of the original text of the summary, has the generated summary content richer and more comprehensive with the generation of duplicate and redundant contents effectively reduced, and has Rouge-2 and Rouge-4 indicators increased by 1.5% and 1.2% respectively.