We present our method for tackling a legal case retrieval task by introducing our method of encoding documents by summarizing them into continuous vector space via our phrase scoring framework utilizing deep neural networks. On the other hand, we explore the benefits from combining lexical features and latent features generated with neural networks. Our experiments show that lexical features and latent features generated with neural networks complement each other to improve the retrieval system performance. Furthermore, our experimental results suggest the importance of case summarization in different aspects: using provided summaries and performing encoded summarization. Our approach achieved F1 of 65.6% and 57.6% on the experimental datasets of legal case retrieval tasks.

翻译：我们提出了一种通过短语评分框架，利用深度神经网络将文档编码至连续向量空间以实现摘要的方法，用于解决法律案例检索任务。同时，我们探索了结合词汇特征与神经网络生成的潜在特征所带来的优势。实验表明，词汇特征与神经网络生成的潜在特征相互补充，从而提升了检索系统的性能。此外，我们的实验结果揭示了案例摘要在不同维度上的重要性：包括使用提供的摘要以及执行编码摘要。我们的方法在法律案例检索任务的实验数据集上分别达到了65.6%和57.6%的F1分数。