民事裁判文书两阶段式自动摘要研究

Automatic Abstracting Civil Judgment Documents with Two-Stage Procedure

【目的】针对民事一审裁判文书内容进行文本自动摘要,为裁判文书的用户提供简练可读、连贯通顺和准确高效的摘要文本。【方法】提出一种面向裁判文书自动摘要的新方法,该方法由抽取式摘要和生成式摘要两个阶段构成。在第一阶段抽取式摘要中,在预训练模型的基础上加入膨胀残差门控卷积神经网络进行裁判文书关键句子抽取得到抽取式文摘;在第二阶段生成式摘要中,将抽取式文摘作为模型的输入,通过序列到序列模型生成最终的裁判文书摘要。【结果】本文所提模型在裁判文书自动摘要实验中的ROUGE指标分别是50.31、36.60、48.86,较基准模型LEAD-3分别提高25.00、23.25、24.66。【局限】将第一阶段得到的抽取式摘要作为第二阶段生成式模型的输入,存在模型的累计误差,模型的整体效果受到第一阶段抽取式模型的影响。【结论】本文模型可以有效地应用在裁判文书自动摘要服务中,解决裁判文书信息过载问题,为裁判文书用户提供了一种快速阅读裁判文书、获取知识的新途径。

[Objective] This paper tries to automatically summarize the contents of civil judgment documents in the first-instance, aiming to provide concise, readable, coherent, accurate and efficient knowledge services.[Methods] We proposed an automatic abstracting method for judgment documents, which includes extractive summary stage and abstract summary stage. We first added the expanded residual gate convolution to the pretraining model to extract key sentences from the judgment documents. Then, we input the extractive summary to the sequence to sequence model and generated the final judgment document abstracts. [Results] The ROUGE indicators of the proposed model were 50.31, 36.60, and 48.86 with the experimental data sets of judgment documents, which were 25.00, 23.25, 24.66 higher than the results of the benchmark model(LEAD-3).[Limitations] The extractive summary obtained in the first stage is used as the input of the second stage abstract model, which creates cumulative error issue. The overall performance of the proposed model is decided by the extractive model of the first stage. [Conclusions] The proposed model could summarize judgment texts automatically, which solve the information overload issue and help users quickly read judgment documents.