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arXiv · 2512.24997

Classifying long legal documents using short random chunks

Luis Adrián Cabrera-Diego
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Abstract

Classifying legal documents is a challenge, besides their specialized vocabulary, sometimes they can be very long. This means that feeding full documents to a Transformers-based models for classification might be impossible, expensive or slow. Thus, we present a legal document classifier based on DeBERTa V3 and a LSTM, that uses as input a collection of 48 randomly-selected short chunks (max 128 tokens). Besides, we present its deployment pipeline using Temporal, a durable execution solution, which allow us to have a reliable and robust processing workflow. The best model had a weighted F-score of 0.898, while the pipeline running on CPU had a processing median time of 498 seconds per 100 files.

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Luis Adrián Cabrera-Diego. 2025-12-31. Classifying long legal documents using short random chunks. https://arxiv.org/abs/2512.24997

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