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.
@article{arxiv.2307.05081,
title = {Argumentative Segmentation Enhancement for Legal Summarization},
author = {Huihui Xu and Kevin Ashley},
journal= {arXiv preprint arXiv:2307.05081},
year = {2023}
}