# Analysing the Resourcefulness of the Paragraph for Precedence Retrieval

arXiv: https://arxiv.org/abs/2308.01203 · HTML: https://mlsanity.com/paper/2308.01203/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 20 references that are in the map.
- First posted on arXiv 29 Jul 2023.

## Builds on (most cited first, 5 of 5)

- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [node2vec: Scalable Feature Learning for Networks](https://mlsanity.com/paper/1607.00653/) (2016)
- [Legal Case Document Similarity: You Need Both Network and Text](https://mlsanity.com/paper/2209.12474/) (2022)
- [Hier-SPCNet: A Legal Statute Hierarchy-based Heterogeneous Network for Computing Legal Case Document Similarity](https://mlsanity.com/paper/2007.03225/) (2020)
- [Methods for Computing Legal Document Similarity: A Comparative Study](https://mlsanity.com/paper/2004.12307/) (2020)

## Abstract (arXiv)

Developing methods for extracting relevant legal information to aid legal practitioners is an active research area. In this regard, research efforts are being made by leveraging different kinds of information, such as meta-data, citations, keywords, sentences, paragraphs, etc. Similar to any text document, legal documents are composed of paragraphs. In this paper, we have analyzed the resourcefulness of paragraph-level information in capturing similarity among judgments for improving the performance of precedence retrieval. We found that the paragraph-level methods could capture the similarity among the judgments with only a few paragraph interactions and exhibit more discriminating power over the baseline document-level method. Moreover, the comparison results on two benchmark datasets for the precedence retrieval on the Indian supreme court judgments task show that the paragraph-level methods exhibit comparable performance with the state-of-the-art methods