# Automated Extraction of Sentencing Decisions from Court Cases in the Hebrew Language

arXiv: https://arxiv.org/abs/2110.12383 · HTML: https://mlsanity.com/paper/2110.12383/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 20 references that are in the map.
- First posted on arXiv 24 Oct 2021.

## Builds on (most cited first, 3 of 3)

- [How Does NLP Benefit Legal System: A Summary of Legal Artificial Intelligence](https://mlsanity.com/paper/2004.12158/) (2020)
- [AlephBERT:A Hebrew Large Pre-Trained Language Model to Start-off your Hebrew NLP Application With](https://mlsanity.com/paper/2104.04052/) (2021)
- [Legal Area Classification: A Comparative Study of Text Classifiers on Singapore Supreme Court Judgments](https://mlsanity.com/paper/1904.06470/) (2019)

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

- [Leveraging Collection-Wide Similarities for Unsupervised Document Structure Extraction](https://mlsanity.com/paper/2402.13906/) (2024)
- [CanLegalRAGBench: Evaluating Retrieval-Augmented Generation on Canadian Case Law](https://mlsanity.com/paper/2605.30497/) (2026)
- [The Perfect Victim: Computational Analysis of Judicial Attitudes towards Victims of Sexual Violence](https://mlsanity.com/paper/2305.05302/) (2023)

## Abstract (arXiv)

We present the task of Automated Punishment Extraction (APE) in sentencing decisions from criminal court cases in Hebrew. Addressing APE will enable the identification of sentencing patterns and constitute an important stepping stone for many follow up legal NLP applications in Hebrew, including the prediction of sentencing decisions. We curate a dataset of sexual assault sentencing decisions and a manually-annotated evaluation dataset, and implement rule-based and supervised models. We find that while supervised models can identify the sentence containing the punishment with good accuracy, rule-based approaches outperform them on the full APE task. We conclude by presenting a first analysis of sentencing patterns in our dataset and analyze common models' errors, indicating avenues for future work, such as distinguishing between probation and actual imprisonment punishment. We will make all our resources available upon request, including data, annotation, and first benchmark models.