# Automatic Text Summarization of Legal Cases: A Hybrid Approach

arXiv: https://arxiv.org/abs/1908.09119 · HTML: https://mlsanity.com/paper/1908.09119/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Aug 2019.

## Built on by (most cited first, 3 of 3)

- [Robust Deep Reinforcement Learning for Extractive Legal Summarization](https://mlsanity.com/paper/2111.07158/) (2021)
- [Combining topic modelling and citation network analysis to study case law from the European Court on Human Rights on the right to respect for private and family life](https://mlsanity.com/paper/2401.16429/) (2024)
- [An explainable approach to detect case law on housing and eviction issues within the HUDOC database](https://mlsanity.com/paper/2410.02978/) (2024)

## Abstract (arXiv)

Manual Summarization of large bodies of text involves a lot of human effort and time, especially in the legal domain. Lawyers spend a lot of time preparing legal briefs of their clients' case files. Automatic Text summarization is a constantly evolving field of Natural Language Processing(NLP), which is a subdiscipline of the Artificial Intelligence Field. In this paper a hybrid method for automatic text summarization of legal cases using k-means clustering technique and tf-idf(term frequency-inverse document frequency) word vectorizer is proposed. The summary generated by the proposed method is compared using ROGUE evaluation parameters with the case summary as prepared by the lawyer for appeal in court. Further, suggestions for improving the proposed method are also presented.