Legal research is a vital part of the work of lawyers. The increasing complexity of legal cases has led to a desire for fast and accurate legal information retrieval, leveraging semantic information. However, two main problems occur on that path. First, the share of published judgments is only marginal. Second, it lacks state- of-the-art NLP approaches to extract semantic information. The latter, in turn, can be attributed to the issue of data scarcity. One big issue in the publication process of court rulings is the lack of automatization. Yet, the digitalization of court rulings, specifically transforming the textual representation from the court into a machine-readable format, is mainly done manually. To address this issue, we propose an automated pipeline to segment court rulings and extract metadata. We integrate that pipeline into a prototypical web application and use it for a qualitative evaluation. The results show that the extraction of metadata and the classification of paragraphs into the respective verdict segments perform well and can be utilized within the existing processes at legal publishers.
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