Intelligent legal judgment method based on text extraction

In recent years, deep learning has achieved great success in natural language processing. The convolutional neural network has been widely used in legal intelligent decisions. Based on the Bidirectional Encoder Representations from Transformers(BERT) model, it can extract the bidirectional context information in the legal statement text, and complete multiple judicial intelligent judgment tasks such as crime prediction, legal clause recommendation and sentence prediction through the self-attention mechanism in BERT. In order to further solve the length limitation of the BERT model on the input text and obtain better results on long text legal statement samples, extract long legal statements. In the process of text extraction, the pre-trained BERT model is used to evaluate the importance of sentences in legal statements, and key sentences are extracted for redundant legal statements to reduce the input length of the judgment model and remove irrelevant information. The extracted text is sent to the BERT model for judicial trial. Compared with the original methods, the legal statement based on text extraction can achieve better results in the intelligent trial task.