# Legal Search in Case Law and Statute Law

arXiv: https://arxiv.org/abs/2108.10127 · HTML: https://mlsanity.com/paper/2108.10127/

## 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 (most cited first, 6 of 6)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation](https://mlsanity.com/paper/1609.08144/) (2016)
- [What Does BERT Look At? An Analysis of BERT's Attention](https://mlsanity.com/paper/1906.04341/) (2019)
- [Open Sesame: Getting Inside BERT's Linguistic Knowledge](https://mlsanity.com/paper/1906.01698/) (2019)
- [Universal Language Model Fine-tuning for Text Classification](https://mlsanity.com/paper/1801.06146/) (2018)
- [Variations of the Similarity Function of TextRank for Automated Summarization](https://mlsanity.com/paper/1602.03606/) (2016)

## Built on by (most cited first, 2 of 2)

- [Discovering Explanatory Sentences in Legal Case Decisions Using Pre-trained Language Models](https://mlsanity.com/paper/2112.07165/) (2021)
- [Cross-Domain Generalization and Knowledge Transfer in Transformers Trained on Legal Data](https://mlsanity.com/paper/2112.07870/) (2021)

## 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.