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The Brazilian judiciary has a large workload, resulting in a long time tofinish legal proceedings. Brazilian National Council of Justice has establishedin Resolution 469/2022 formal guidance for document and process digitalizationopening up the possibility of using automatic techniques to help with everydaytasks in the legal field, particularly in a large number of texts yielded onthe routine of law procedures. Notably, Artificial Intelligence (AI) techniquesallow for processing and extracting useful information from textual data,potentially speeding up the process. However, datasets from the legal domainrequired by several AI techniques are scarce and difficult to obtain as theyneed labels from experts. To address this challenge, this article contributeswith four datasets from the legal domain, two with documents and metadata butunlabeled, and another two labeled with a heuristic aiming at its use intextual semantic similarity tasks. Also, to evaluate the effectiveness of theproposed heuristic label process, this article presents a small ground truthdataset generated from domain expert annotations. The analysis of ground truthlabels highlights that semantic analysis of domain text can be challenging evenfor domain experts. Also, the comparison between ground truth and heuristiclabels shows that heuristic labels are useful.

Datasets for Portuguese Legal Semantic Textual Similarity: Comparing weak supervision and an annotation process approaches