LegalSeg: Unlocking the Structure of Indian Legal Judgments Through Rhetorical Role Classification

TL;DR

This paper introduces LegalSeg, a large annotated dataset for classifying rhetorical roles in Indian legal judgments, and evaluates various models to improve legal document understanding.

Contribution

LegalSeg is the largest dataset for rhetorical role classification in Indian legal judgments, enabling benchmarking of advanced NLP models in this domain.

Findings

Models with broader context outperform sentence-only models.

Structural and sequential information improve classification accuracy.

Challenges remain in distinguishing similar roles and handling class imbalance.

Abstract

In this paper, we address the task of semantic segmentation of legal documents through rhetorical role classification, with a focus on Indian legal judgments. We introduce LegalSeg, the largest annotated dataset for this task, comprising over 7,000 documents and 1.4 million sentences, labeled with 7 rhetorical roles. To benchmark performance, we evaluate multiple state-of-the-art models, including Hierarchical BiLSTM-CRF, TransformerOverInLegalBERT (ToInLegalBERT), Graph Neural Networks (GNNs), and Role-Aware Transformers, alongside an exploratory RhetoricLLaMA, an instruction-tuned large language model. Our results demonstrate that models incorporating broader context, structural relationships, and sequential sentence information outperform those relying solely on sentence-level features. Additionally, we conducted experiments using surrounding context and predicted or actual labels of neighboring sentences to assess their impact on classification accuracy. Despite these advancements, challenges persist in distinguishing between closely related roles and addressing class imbalance. Our work underscores the potential of advanced techniques for improving legal document understanding and sets a strong foundation for future research in legal NLP.