Classification of Legal Documents in Portuguese Language Based on Summarization

• Published in: 2022 IEEE Latin American Conference on Computational Intelligence (LA-CCI) pp. 1 - 6
• Main Authors: Medina, Marie Chantelle Cruz, Da Silva Oliveira, Lucas Matheus, Ferreira, Jean Felipe Coelho, Silva, Leandro Honorato De S., Rodrigues, Cleyton Mario O., De Oliveira, Joao Fausto L., Sobral, Paulo Christiano, Souza, Bruno, Feitosa, Dionizio, Fernandes, Bruno J. T.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 23.11.2022
• Subjects: Bag of Words; Classification algorithms; Computational modeling; Feature extraction; Law; Summarization; Support vector machine classification; Text categorization; Text Classification; Training
• DOI: 10.1109/LA-CCI54402.2022.9981852

Legal document classification in Portuguese language is a research area highly benefited by computational intelligence techniques as the availability of better processing with the easiness of digital text recording of juridic processes. Different techniques have been explored to achieve reliable results in real-world conditions; however, the most suitable configuration of methods remains to be an open problem. This study proposes a model consisting of four stages: preprocessing, extractive summarization using page rank algorithm, feature extraction with bag-of-words, and classification with Support Vector Classifier. Testing sessions were conducted using three versions of the model as a mean for comparison and evaluation. The first one was a basic classifier without preprocessing nor summarization stages, the second included preprocessing but not summarization, and the third one was an implementation of the complete proposed model. All three were evaluated using a separated set of examples falling into six different labeled categories and their performance was recorded calculating weighted average precision, recall, F1-score and accuracy values. The best performance obtained was the one presented by the proposed model with precision, recall and F-1 score values of 96% each, which represents a 2% improvement for all of them in comparison to the first version and a 1 % improvement for precision and recall in comparison to the second version. Specially F1-score pointing to the most balanced performance, the proposed model outperformed the versions of it itself excluding some stages, allowing to infer that preprocessing and extractive summarization have positive impacts in the text classification task for Portuguese-written legal documents.