# Plain English Summarization of Contracts

arXiv: https://arxiv.org/abs/1906.00424 · HTML: https://mlsanity.com/paper/1906.00424/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Jun 2019.

## Built on by (most cited first, 28 of 28)

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- [Robust Deep Reinforcement Learning for Extractive Legal Summarization](https://mlsanity.com/paper/2111.07158/) (2021)
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- [VLQA: The First Comprehensive, Large, and High-Quality Vietnamese Dataset for Legal Question Answering](https://mlsanity.com/paper/2507.19995/) (2025)
- [Fairness-aware Summarization for Justified Decision-Making](https://mlsanity.com/paper/2107.06243/) (2021)
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- [How Private are Language Models in Abstractive Summarization?](https://mlsanity.com/paper/2412.12040/) (2024)
- [VieSum: How Robust Are Transformer-based Models on Vietnamese Summarization?](https://mlsanity.com/paper/2110.04257/) (2021)
- [Analyzing Hong Kong's Legal Judgments from a Computational Linguistics point-of-view](https://mlsanity.com/paper/2305.02558/) (2023)
- [The Medium Is Not the Message: Deconfounding Document Embeddings via Linear Concept Erasure](https://mlsanity.com/paper/2507.01234/) (2025)
- [CourtPressGER: A German Court Decision to Press Release Summarization Dataset](https://mlsanity.com/paper/2512.09434/) (2025)
- [Evaluation of Large Language Models in Legal Applications: Challenges, Methods, and Future Directions](https://mlsanity.com/paper/2601.15267/) (2026)
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## Abstract (arXiv)

Unilateral contracts, such as terms of service, play a substantial role in modern digital life. However, few users read these documents before accepting the terms within, as they are too long and the language too complicated. We propose the task of summarizing such legal documents in plain English, which would enable users to have a better understanding of the terms they are accepting. We propose an initial dataset of legal text snippets paired with summaries written in plain English. We verify the quality of these summaries manually and show that they involve heavy abstraction, compression, and simplification. Initial experiments show that unsupervised extractive summarization methods do not perform well on this task due to the level of abstraction and style differences. We conclude with a call for resource and technique development for simplification and style transfer for legal language.