Legal Search in Case Law and Statute Law
This addresses legal search efficiency for practitioners, but it is incremental as it builds on existing language models.
The paper tackled the problem of identifying pairwise relevance between legal documents with limited resources, long queries, and long documents, and found that their method using text summaries outperformed existing baselines based on full text.
In this work we describe a method to identify document pairwise relevance in the context of a typical legal document collection: limited resources, long queries and long documents. We review the usage of generalized language models, including supervised and unsupervised learning. We observe how our method, while using text summaries, overperforms existing baselines based on full text, and motivate potential improvement directions for future work.