  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.
