Argumentative Segmentation Enhancement for Legal Summarization
This work addresses legal summarization for legal professionals, but it is incremental as it builds on existing argumentative zoning and GPT models.
The authors tackled the problem of generating high-quality legal summaries by using argumentative segmentation to classify segments of legal case decisions, and found that their method produced higher quality argumentative summaries compared to GPT-4 and non-GPT models.
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.