  We use the combination of argumentative zoning [1] and a legal argumentative
scheme to create legal argumentative segments. Based on the argumentative
segmentation, we propose a novel task of classifying argumentative segments of
legal case decisions. GPT-3.5 is used to generate summaries based on
argumentative segments. In terms of automatic evaluation metrics, our method
generates higher quality argumentative summaries while leaving out less
relevant context as compared to GPT-4 and non-GPT models.
