# Citation-Based Summarization of Landmark Judgments

arXiv: https://arxiv.org/abs/2406.10824 · HTML: https://mlsanity.com/paper/2406.10824/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 20 references that are in the map.
- First posted on arXiv 16 Jun 2024.

## Builds on (most cited first, 6 of 6)

- [LexGLUE: A Benchmark Dataset for Legal Language Understanding in English](https://mlsanity.com/paper/2110.00976/) (2021)
- [When Does Pretraining Help? Assessing Self-Supervised Learning for Law and the CaseHOLD Dataset](https://mlsanity.com/paper/2104.08671/) (2021)
- [Named Entity Recognition in Indian court judgments](https://mlsanity.com/paper/2211.03442/) (2022)
- [Incorporating Domain Knowledge for Extractive Summarization of Legal Case Documents](https://mlsanity.com/paper/2106.15876/) (2021)
- [LawSum: A weakly supervised approach for Indian Legal Document Summarization](https://mlsanity.com/paper/2110.01188/) (2021)
- [Indian Legal Text Summarization: A Text Normalisation-based Approach](https://mlsanity.com/paper/2206.06238/) (2022)

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

- [A Comprehensive Survey on Legal Summarization: Challenges and Future Directions](https://mlsanity.com/paper/2501.17830/) (2025)
- [AugAbEx: Bridging Abstractive and Extractive Legal Summarization](https://mlsanity.com/paper/2511.12290/) (2025)

## Abstract (arXiv)

Landmark judgments are of prime importance in the Common Law System because of their exceptional jurisprudence and frequent references in other judgments. In this work, we leverage contextual references available in citing judgments to create an extractive summary of the target judgment. We evaluate the proposed algorithm on two datasets curated from the judgments of Indian Courts and find the results promising.