[Objective] This paper tries to automatically summarize the contents of civil judgment documents in the first-instance, aiming to provide concise, readable, coherent, accurate and efficient knowledge services. [Methods] We proposed an automatic abstracting method for judgment documents, which includes extractive summary stage and abstract summary stage. We first added the expanded residual gate convolution to the pre-training model to extract key sentences from the judgment documents. Then, we input the extractive summary to the sequence to sequence model and generated the final judgment document abstracts. [Results] The ROUGE indicators of the proposed model were 50.31, 36.60, and 48.86 with the experimental data sets of judgment documents, which were 25.00, 23.25, 24.66 higher than the results of the benchmark model (LEAD-3). [Limitations] The extractive summary obtained in the first stage is used as the input of the second stage abstract model, which creates cumulative error issue. The overall performance of the proposed model is decided by the extractive model of the first stage. [Conclusions] The proposed model could summarize judgment texts automatically, which solve the information overload issue and help users quickly read judgment documents.
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