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At present,the automatic summarization model for Chinese content applied to legal judgement documents mainly adopts the extraction method.However,due to the lengthiness and low level of structure of legal texts,the accuracy and reliability of extraction method is insufficient for practical application.In order to obtain high quality summaries of legal judgment documents,in this paper,we propose an abstractive automatic summarization model based on multi-model fusion.Based on Seq2Seq model,we apply attention mechanism and selective gates to better process the data input.Specifically,we combine Bert pre-trai-ning and reinforcement learning policy to optimize our model.The corpus we built consists of 50 000 legal judgment documents regarding small claims procedure and summary procedure.Evaluations on the corpus demonstrate that the proposed model outperforms all of the baseline model,and the mean ROUGE score is 5.81% higher than that of conventional Seq2seq+Attentionmodel.