# LawSum: A weakly supervised approach for Indian Legal Document Summarization

arXiv: https://arxiv.org/abs/2110.01188 · HTML: https://mlsanity.com/paper/2110.01188/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 46 references that are in the map.
- First posted on arXiv 4 Oct 2021.

## Builds on (most cited first, 13 of 13)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Big Bird: Transformers for Longer Sequences](https://mlsanity.com/paper/2007.14062/) (2020)
- [Text Summarization with Pretrained Encoders](https://mlsanity.com/paper/1908.08345/) (2019)
- [SummaRuNNer: A Recurrent Neural Network based Sequence Model for Extractive Summarization of Documents](https://mlsanity.com/paper/1611.04230/) (2016)
- [HIBERT: Document Level Pre-training of Hierarchical Bidirectional Transformers for Document Summarization](https://mlsanity.com/paper/1905.06566/) (2019)
- [Neural Summarization by Extracting Sentences and Words](https://mlsanity.com/paper/1603.07252/) (2016)
- [Sentence Centrality Revisited for Unsupervised Summarization](https://mlsanity.com/paper/1906.03508/) (2019)
- [BottleSum: Unsupervised and Self-supervised Sentence Summarization using the Information Bottleneck Principle](https://mlsanity.com/paper/1909.07405/) (2019)
- [Identification of Rhetorical Roles of Sentences in Indian Legal Judgments](https://mlsanity.com/paper/1911.05405/) (2019)
- [A Supervised Approach to Extractive Summarisation of Scientific Papers](https://mlsanity.com/paper/1706.03946/) (2017)
- [Incorporating Domain Knowledge for Extractive Summarization of Legal Case Documents](https://mlsanity.com/paper/2106.15876/) (2021)
- [Simple Unsupervised Summarization by Contextual Matching](https://mlsanity.com/paper/1907.13337/) (2019)

## Built on by (most cited first, 6 of 6)

- [A Comprehensive Survey on Process-Oriented Automatic Text Summarization with Exploration of LLM-Based Methods](https://mlsanity.com/paper/2403.02901/) (2024)
- [A Comprehensive Survey on Legal Summarization: Challenges and Future Directions](https://mlsanity.com/paper/2501.17830/) (2025)
- [Indian Legal Text Summarization: A Text Normalisation-based Approach](https://mlsanity.com/paper/2206.06238/) (2022)
- [Citation-Based Summarization of Landmark Judgments](https://mlsanity.com/paper/2406.10824/) (2024)
- [AugAbEx: Bridging Abstractive and Extractive Legal Summarization](https://mlsanity.com/paper/2511.12290/) (2025)
- [IMLJD: A Computational Dataset for Indian Matrimonial Litigation Analysis](https://mlsanity.com/paper/2605.19346/) (2026)

## Abstract (arXiv)

Unlike the courts in western countries, public records of Indian judiciary are completely unstructured and noisy. No large scale publicly available annotated datasets of Indian legal documents exist till date. This limits the scope for legal analytics research. In this work, we propose a new dataset consisting of over 10,000 judgements delivered by the supreme court of India and their corresponding hand written summaries. The proposed dataset is pre-processed by normalising common legal abbreviations, handling spelling variations in named entities, handling bad punctuations and accurate sentence tokenization. Each sentence is tagged with their rhetorical roles. We also annotate each judgement with several attributes like date, names of the plaintiffs, defendants and the people representing them, judges who delivered the judgement, acts/statutes that are cited and the most common citations used to refer the judgement. Further, we propose an automatic labelling technique for identifying sentences which have summary worthy information. We demonstrate that this auto labeled data can be used effectively to train a weakly supervised sentence extractor with high accuracy. Some possible applications of this dataset besides legal document summarization can be in retrieval, citation analysis and prediction of decisions by a particular judge.