Abstract
This paper studies the compilation of judicial case summarisation in China. Judicial case summaries are made through the abridgement, generalisation, and summarisation of court verdicts. It is a time-consuming, inefficient manual process done by legal professionals. The automatic generation of such summaries could save much time of legal professionals. Court verdicts are generally lengthy, exceeding the maximum word limit for inputs into pre-trained models. Through the observation and analysis of existing data sets, this paper conducts further treatment of these datasets. The dataset of one court verdict is split into five via phrase extraction to obtain the extracts of five key components of a court verdict and the corresponding manual summaries. In this way, we convert one text summarisation problem into five text compression and integration problems for sentences of five different categories. We adopt the GPT-2 pre-trained model, which excels in text generation, to conduct text compression and integration. From that, key points for compression of various parts of the verdict are obtained, which are eventually put together to obtain the summary of the court verdict. This paper divides datasets using extractive algorithms and compresses and integrates them using abstractive algorithms. Our experiments show that our approach proposed by this paper performs well.
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Acknowledgements
This work was supported by the National Natural Science Foundation of China (No. 61762016), and a research fund of Guangxi Key Lab of Multi-Source Information Mining & Security (No. 19-A-01-01).
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Liu, J., Wu, J., Luo, X. (2021). Chinese Judicial Summarising Based on Short Sentence Extraction and GPT-2. In: Qiu, H., Zhang, C., Fei, Z., Qiu, M., Kung, SY. (eds) Knowledge Science, Engineering and Management . KSEM 2021. Lecture Notes in Computer Science(), vol 12816. Springer, Cham. https://doi.org/10.1007/978-3-030-82147-0_31
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