Argumentative Segmentation Enhancement for Legal Summarization
Huihui Xu, Kevin Ashley
TL;DR
This paper introduces a method combining argumentative zoning and legal schemes to segment legal texts, enabling GPT-3.5 to generate more focused legal summaries with improved quality over other models.
Contribution
It presents a novel approach for legal argumentative segmentation and a new task of classifying these segments for better legal summarization.
Findings
Higher quality argumentative summaries compared to GPT-4 and non-GPT models
Effective segmentation improves relevance in legal summaries
Method enhances automatic evaluation metrics for legal summarization
Abstract
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.
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Taxonomy
TopicsArtificial Intelligence in Law · Natural Language Processing Techniques · Multi-Agent Systems and Negotiation
Methods{Dispute@FaQ-s}How to file a dispute with Expedia? · Attention Is All You Need · Position-Wise Feed-Forward Layer · Absolute Position Encodings · Label Smoothing · Transformer · Cosine Annealing · Linear Layer · Weight Decay · Adam