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We present the task of Automated Punishment Extraction (APE) in sentencingdecisions from criminal court cases in Hebrew. Addressing APE will enable theidentification of sentencing patterns and constitute an important steppingstone for many follow up legal NLP applications in Hebrew, including theprediction of sentencing decisions. We curate a dataset of sexual assaultsentencing decisions and a manually-annotated evaluation dataset, and implementrule-based and supervised models. We find that while supervised models canidentify the sentence containing the punishment with good accuracy, rule-basedapproaches outperform them on the full APE task. We conclude by presenting afirst analysis of sentencing patterns in our dataset and analyze common models'errors, indicating avenues for future work, such as distinguishing betweenprobation and actual imprisonment punishment. We will make all our resourcesavailable upon request, including data, annotation, and first benchmark models.

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