Extractive-Abstractive Summarization of Judgment Documents Using Multiple Attention Networks

Judgment documents contain rich legal information, they are simultaneously lengthy with complex structure. This requires summarizing judgment documents in an effective way. By analyzing the structural features of Chinese judgment documents, we propose an automatic summarization method, which consists of an extraction model and an abstraction model. In the extraction model, all the sentences are encoded by a Self-Attention network and are classified into key sentences and non-key sentences. In the abstraction model, the initial summarization is refined into a final summarization by a unidirectional-bidirectional attention network. Such a summarization could help improve the efficiency in case handling and make judgment documents more accessible to the general readers. The experimental results on CAIL2020 dataset are satisfactory.