# Semantic Segmentation of Legal Documents via Rhetorical Roles

arXiv: https://arxiv.org/abs/2112.01836 · HTML: https://mlsanity.com/paper/2112.01836/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 37 references that are in the map.
- First posted on arXiv 3 Dec 2021.

## Builds on (most cited first, 11 of 11)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Self-training with Noisy Student improves ImageNet classification](https://mlsanity.com/paper/1911.04252/) (2019)
- [Multi-Task Learning with Deep Neural Networks: A Survey](https://mlsanity.com/paper/2009.09796/) (2020)
- [Neural Legal Judgment Prediction in English](https://mlsanity.com/paper/1906.02059/) (2019)
- [ILDC for CJPE: Indian Legal Documents Corpus for Court Judgment Prediction and Explanation](https://mlsanity.com/paper/2105.13562/) (2021)
- [Legal Judgment Prediction via Multi-Perspective Bi-Feedback Network](https://mlsanity.com/paper/1905.03969/) (2019)
- [Identification of Rhetorical Roles of Sentences in Indian Legal Judgments](https://mlsanity.com/paper/1911.05405/) (2019)
- [Interpretable Charge Predictions for Criminal Cases: Learning to Generate Court Views from Fact Descriptions](https://mlsanity.com/paper/1802.08504/) (2018)
- [Charge-Based Prison Term Prediction with Deep Gating Network](https://mlsanity.com/paper/1908.11521/) (2019)
- [Building Legal Case Retrieval Systems with Lexical Matching and Summarization using A Pre-Trained Phrase Scoring Model](https://mlsanity.com/paper/2009.14083/) (2020)

## Built on by (most cited first, 26 of 26)

- [Corpus for Automatic Structuring of Legal Documents](https://mlsanity.com/paper/2201.13125/) (2022)
- [U-CREAT: Unsupervised Case Retrieval using Events extrAcTion](https://mlsanity.com/paper/2307.05260/) (2023)
- [HLDC: Hindi Legal Documents Corpus](https://mlsanity.com/paper/2204.00806/) (2022)
- [Fact-based Court Judgment Prediction](https://mlsanity.com/paper/2311.13350/) (2023)
- [Computing and Exploiting Document Structure to Improve Unsupervised Extractive Summarization of Legal Case Decisions](https://mlsanity.com/paper/2211.03229/) (2022)
- [Harnessing GPT-3.5-turbo for Rhetorical Role Prediction in Legal Cases](https://mlsanity.com/paper/2310.17413/) (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)
- [HiCuLR: Hierarchical Curriculum Learning for Rhetorical Role Labeling of Legal Documents](https://mlsanity.com/paper/2409.18647/) (2024)
- [NyayaRAG: Realistic Legal Judgment Prediction with RAG under the Indian Common Law System](https://mlsanity.com/paper/2508.00709/) (2025)
- [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)
- [IL-PCSR: Legal Corpus for Prior Case and Statute Retrieval](https://mlsanity.com/paper/2511.00268/) (2025)
- [NyayaAnumana & INLegalLlama: The Largest Indian Legal Judgment Prediction Dataset and Specialized Language Model for Enhanced Decision Analysis](https://mlsanity.com/paper/2412.08385/) (2024)
- [EDUKG: a Heterogeneous Sustainable K-12 Educational Knowledge Graph](https://mlsanity.com/paper/2210.12228/) (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)
- [Learning functional sections in medical conversations: iterative pseudo-labeling and human-in-the-loop approach](https://mlsanity.com/paper/2210.02658/) (2022)
- [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)
- [Structured Legal Document Generation in India: A Model-Agnostic Wrapper Approach with VidhikDastaavej](https://mlsanity.com/paper/2504.03486/) (2025)
- [ReGal: A First Look at PPO-based Legal AI for Judgment Prediction and Summarization in India](https://mlsanity.com/paper/2512.18014/) (2025)
- [SegNSP: Revisiting Next Sentence Prediction for Linear Text Segmentation](https://mlsanity.com/paper/2601.03474/) (2026)
- [Rhetorical-Role-Aware Retrieval-Augmented Generation for Legal Question Answering over Indian Supreme Court Judgments](https://mlsanity.com/paper/2608.06828/) (2026)
- [Mining Legal Arguments in U.S. Corporate Case Law](https://mlsanity.com/paper/2609.25441/) (2026)

## Abstract (arXiv)

Legal documents are unstructured, use legal jargon, and have considerable length, making them difficult to process automatically via conventional text processing techniques. A legal document processing system would benefit substantially if the documents could be segmented into coherent information units. This paper proposes a new corpus of legal documents annotated (with the help of legal experts) with a set of 13 semantically coherent units labels (referred to as Rhetorical Roles), e.g., facts, arguments, statute, issue, precedent, ruling, and ratio. We perform a thorough analysis of the corpus and the annotations. For automatically segmenting the legal documents, we experiment with the task of rhetorical role prediction: given a document, predict the text segments corresponding to various roles. Using the created corpus, we experiment extensively with various deep learning-based baseline models for the task. Further, we develop a multitask learning (MTL) based deep model with document rhetorical role label shift as an auxiliary task for segmenting a legal document. The proposed model shows superior performance over the existing models. We also experiment with model performance in the case of domain transfer and model distillation techniques to see the model performance in limited data conditions.