Legal Search in Case Law and Statute Law
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Abstract (arXiv)
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.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 6 of its 23 references that are in the map. First posted on arXiv 23 Aug 2021.
Builds on
6 of 23 references.
References · 6
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
- 2016Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translationcited by 1,475
- 2019What Does BERT Look At? An Analysis of BERT's Attentioncited by 1,003
- 2019Open Sesame: Getting Inside BERT's Linguistic Knowledgecited by 120
- 2018Universal Language Model Fine-tuning for Text Classificationcited by 52
- 2016Variations of the Similarity Function of TextRank for Automated Summarizationcited by 27
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Citing papers per year: 2021: 2.
2 papers.