Encoded Summarization: Summarizing Documents into Continuous Vector Space for Legal Case Retrieval
This work addresses legal case retrieval for legal professionals, but it is incremental as it combines existing lexical and neural features.
The paper tackled legal case retrieval by summarizing documents into continuous vector space using a phrase scoring framework with deep neural networks, achieving F1 scores of 65.6% and 57.6% on experimental datasets.
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.