Encoded summarization: summarizing documents into continuous vector space for legal case retrieval

Artificial Intelligence and Law - Tập 28 - Trang 441-467 - 2020
Vu Tran1, Minh Le Nguyen1, Satoshi Tojo1, Ken Satoh2
1Japan Advanced Institute of Science and Technology, Nomi, Japan
2National Institute of Informatics, Tokyo, Japan

Tóm tắt

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.

Tài liệu tham khảo

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