The automatic generation of the draft procuratorial suggestions is to extract the description of illegal facts, administrative omission, description of laws and regulations, and other information from the case documents. Previously, the existing deep learning methods mainly focus on context-free word embeddings when addressing legal domain-speci?c extractive summarization tasks, which cannot get a better semantic understanding of the text and in turn leads to an adverse summarization performance. To this end, we propose a novel deep contextualized embeddings-based method BERTSLCA to conduct the extractive summarization task. The model is mainly based on the variant of BERT called BERTSUM. Firstly, the input document is fed into BERTSUM to get sentence-level embeddings. Then, we design an extracting architecture to catch the long dependency between sentences utilizing the Bi-Long Short-Term Memory (Bi-LSTM) unit, and at the end of the architecture, three cascaded convolution kernels with different sizes are designed to extract the relationships between adjacent sentences. Last, we introduce an attention mechanism to strengthen the ability to distinguish the importance of different sentences. To the best of our knowledge, this is the first work to use the pretrained language model for extractive summarization tasks in the field of Chinese judicial litigation. Experimental results on public interest litigation data and CAIL 2020 dataset all demonstrate that the proposed method achieves competitive performance.
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