Extractive Summarization of Chinese Judgment Documents via Sentence Embedding and Memory Network

A rapidly rising number of open judgment documents has increased the requirement for automatic summarization. Since Chinese judgment documents are characterized by a lengthy and logical structure, extractive summarization is an effective method for them. However, existing extractive models generally cannot capture information between sentences. In order to enable the model to obtain long-term information in the judgment documents, this paper proposes an extractive model using sentence embeddings and a two-layers memory network. A pre-trained language model is used to encode sentences in judgment documents. Then the whitening operation is applied to get isotropic sentence embeddings, which makes the subsequent classification more accurate. These embeddings are fed into a unidirectional memory network to fuse previous sentence embeddings. A bidirectional memory network is followed to introduce position information of sentences. The experimental results show that our proposed model outperforms the baseline methods on the SFZY dataset from CAIL2020.