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.

本文提出了自动制裁提取（APE）的任务，旨在从希伯来语刑事法庭案件中提取制裁决定。我们通过制定规则和监督模型来完成这个任务，并发现在 APE 任务上，规则型方法优于监督模型。我们分析了模型常见的错误，并针对将来的工作提出了建议，如区分缓刑和实际监禁惩罚。