# Identification of Rhetorical Roles of Sentences in Indian Legal Judgments

arXiv: https://arxiv.org/abs/1911.05405 · HTML: https://mlsanity.com/paper/1911.05405/

## Key facts

- Cited by 39 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Nov 2019.

## Built on by (most cited first, 30 of 39)

- [ILDC for CJPE: Indian Legal Documents Corpus for Court Judgment Prediction and Explanation](https://mlsanity.com/paper/2105.13562/) (2021)
- [Pre-trained Language Models for the Legal Domain: A Case Study on Indian Law](https://mlsanity.com/paper/2209.06049/) (2022)
- [Unlocking Practical Applications in Legal Domain: Evaluation of GPT for Zero-Shot Semantic Annotation of Legal Texts](https://mlsanity.com/paper/2305.04417/) (2023)
- [Semantic Segmentation of Legal Documents via Rhetorical Roles](https://mlsanity.com/paper/2112.01836/) (2021)
- [Corpus for Automatic Structuring of Legal Documents](https://mlsanity.com/paper/2201.13125/) (2022)
- [Incorporating Domain Knowledge for Extractive Summarization of Legal Case Documents](https://mlsanity.com/paper/2106.15876/) (2021)
- [LeSICiN: A Heterogeneous Graph-based Approach for Automatic Legal Statute Identification from Indian Legal Documents](https://mlsanity.com/paper/2112.14731/) (2021)
- [IL-TUR: Benchmark for Indian Legal Text Understanding and Reasoning](https://mlsanity.com/paper/2407.05399/) (2024)
- [Lex Rosetta: Transfer of Predictive Models Across Languages, Jurisdictions, and Legal Domains](https://mlsanity.com/paper/2112.07882/) (2021)
- [Fact-based Court Judgment Prediction](https://mlsanity.com/paper/2311.13350/) (2023)
- [Sentence Embeddings and High-speed Similarity Search for Fast Computer Assisted Annotation of Legal Documents](https://mlsanity.com/paper/2112.11494/) (2021)
- [Extractive Summarization of Legal Decisions using Multi-task Learning and Maximal Marginal Relevance](https://mlsanity.com/paper/2210.12437/) (2022)
- [Nonet at SemEval-2023 Task 6: Methodologies for Legal Evaluation](https://mlsanity.com/paper/2310.11049/) (2023)
- [LegalSeg: Unlocking the Structure of Indian Legal Judgments Through Rhetorical Role Classification](https://mlsanity.com/paper/2502.05836/) (2025)
- [SemEval 2023 Task 6: LegalEval - Understanding Legal Texts](https://mlsanity.com/paper/2304.09548/) (2023)
- [LawSum: A weakly supervised approach for Indian Legal Document Summarization](https://mlsanity.com/paper/2110.01188/) (2021)
- [HiCuLR: Hierarchical Curriculum Learning for Rhetorical Role Labeling of Legal Documents](https://mlsanity.com/paper/2409.18647/) (2024)
- [Cross-Domain Generalization and Knowledge Transfer in Transformers Trained on Legal Data](https://mlsanity.com/paper/2112.07870/) (2021)
- [Understand Legal Documents with Contextualized Large Language Models](https://mlsanity.com/paper/2303.12135/) (2023)
- [Mind Your Neighbours: Leveraging Analogous Instances for Rhetorical Role Labeling for Legal Documents](https://mlsanity.com/paper/2404.01344/) (2024)
- [Indian Legal NLP Benchmarks : A Survey](https://mlsanity.com/paper/2107.06056/) (2021)
- [Joint Span Segmentation and Rhetorical Role Labeling with Data Augmentation for Legal Documents](https://mlsanity.com/paper/2302.06448/) (2023)
- [Data-Centric Machine Learning in the Legal Domain](https://mlsanity.com/paper/2201.06653/) (2022)
- [Enhancing Pre-Trained Language Models with Sentence Position Embeddings for Rhetorical Roles Recognition in Legal Opinions](https://mlsanity.com/paper/2310.05276/) (2023)
- [MARRO: Multi-headed Attention for Rhetorical Role Labeling in Legal Documents](https://mlsanity.com/paper/2503.10659/) (2025)
- [Toward an Intelligent Tutoring System for Argument Mining in Legal Texts](https://mlsanity.com/paper/2210.13635/) (2022)
- [Automated Argument Generation from Legal Facts](https://mlsanity.com/paper/2310.05680/) (2023)
- [TathyaNyaya and FactLegalLlama: Advancing Factual Judgment Prediction and Explanation in the Indian Legal Context](https://mlsanity.com/paper/2504.04737/) (2025)
- [Segment First, Retrieve Better: Realistic Legal Search via Rhetorical Role-Based Queries](https://mlsanity.com/paper/2508.00679/) (2025)
- [DALDALL: Data Augmentation for Lexical and Semantic Diverse in Legal Domain by leveraging LLM-Persona](https://mlsanity.com/paper/2603.22765/) (2026)

## Abstract (arXiv)

Automatically understanding the rhetorical roles of sentences in a legal case judgement is an important problem to solve, since it can help in several downstream tasks like summarization of legal judgments, legal search, and so on. The task is challenging since legal case documents are usually not well-structured, and these rhetorical roles may be subjective (as evident from variation of opinions between legal experts). In this paper, we address this task for judgments from the Supreme Court of India. We label sentences in 50 documents using multiple human annotators, and perform an extensive analysis of the human-assigned labels. We also attempt automatic identification of the rhetorical roles of sentences. While prior approaches towards this task used Conditional Random Fields over manually handcrafted features, we explore the use of deep neural models which do not require hand-crafting of features. Experiments show that neural models perform much better in this task than baseline methods which use handcrafted features.