  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.
