Contract Agreement Summarization Task
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A Contract Agreement Summarization Task is a Natural Language Processing (NLP) Task that is a contract-related summarization task specilized in condensing the content of legal contract agreements into concise summaries.
- AKA: Legal Contract Summarization Task.
- Context:
- It can (typically) required a Legal Expert-Annotated NLP Dataset such as Contract Understanding Atticus Dataset (CUAD).
- It can (often) support legal professionals and businesses by automating the process of contract review, reducing the time and effort required to understand complex legal documents.
- It can range from summarizing entire contracts to focusing on specific parts, such as obligations, entitlements, prohibitions, and key dates.
- It can range from being a an Extractive (Contract Agreement) Summarization Task to being an Abstractive (Contract Agreement) Summarization Task.
- ...
- Example(s):
- Plain English (Contract Agreement) Summarization, this converts the complex legal language of a terms of service document into a simplified summary that is easily understandable by non-legal professionals
- Party-Specific (Contract Agreement) Summarization, this generates summaries that highlight obligations, entitlements, and prohibitions specific to each party involved in a contract, such as a tenant and landlord in a lease agreement.
- ...
- Counter-Example(s):
- See: Legal Summarization, Text Summarization, Document Understanding, Legal AI Applications, Contract Review, Legal Document Analysis, Natural Legal Language Processing.
References
2024a
- (ChatGPT-OpenAI, 2024) ⇒ Prompt: Summarize the concept Contract Agreement Summarization Task in 1- 2 paragraphs. ChatGPT (model:gpt-4-turbo-preview). Retrieved: 2024-05-28.
- QUOTE: The Contract Agreement Summarization Task involves the extraction, analysis, and condensation of pertinent information from lengthy and complex contract documents into a succinct, comprehensible summary. This process is crucial for entities needing a clear understanding of contractual obligations, rights, benefits, and potential risks without delving into the entire document's intricacies. Given the dense legal terminology and detailed provisions typically found in contracts, this task requires a keen understanding of legal concepts, attention to detail, and the ability to distill information without losing the essence of the agreement.
The objective is to make the core elements of the contract easily accessible for decision-making, compliance, negotiation, or review purposes. Tools and techniques ranging from manual summarization by legal professionals to advanced AI-driven solutions are employed to automate and enhance the efficiency of this task. Automatic summarization incorporates natural language processing (NLP) and machine learning algorithms to identify significant sections and interpret the legal language, aiming to produce a summary that is both accurate and accessible to non-specialists. This endeavor supports businesses and individuals in managing their contracts more effectively, facilitating a better comprehension of their legal commitments and entitlements.
- QUOTE: The Contract Agreement Summarization Task involves the extraction, analysis, and condensation of pertinent information from lengthy and complex contract documents into a succinct, comprehensible summary. This process is crucial for entities needing a clear understanding of contractual obligations, rights, benefits, and potential risks without delving into the entire document's intricacies. Given the dense legal terminology and detailed provisions typically found in contracts, this task requires a keen understanding of legal concepts, attention to detail, and the ability to distill information without losing the essence of the agreement.
2024b
- (Legal Sifter, 2024) ⇒ "Something Else Not to Use AI for: Summarizing Contracts." In: Adams Contracts, a division of Legal Sifter. Updated 25 February 2024.
- QUOTE: ... But what might make sense for nonfiction writing doesn’t make sense for contracts, for two reasons. First, in contracts, everything matters! It’s like software code—leave something out and bad things can happen.
And second, in the process of summarizing, usually you aren’t able to just prune some words, repeating the rest verbatim. Instead, you likely have to change some words. In the limited and stylized world of contract language, using different words can have significant implications. ...
- QUOTE: ... But what might make sense for nonfiction writing doesn’t make sense for contracts, for two reasons. First, in contracts, everything matters! It’s like software code—leave something out and bad things can happen.
2023
- (Sancheti et al., 2023) ⇒ Abhilasha Sancheti, Aparna Garimella, Balaji Srinivasan, and Rachel Rudinger (2022). "What to Read in a Contract? Party-Specific Summarization of Legal Obligations, Entitlements, and Prohibitions". In: Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing.
- QUOTE: In this work, we propose a new task of party-specific extractive summarization for legal contracts to facilitate faster reviewing and improved comprehension of rights and duties. To facilitate this, we curate a dataset comprising of party-specific pairwise importance comparisons annotated by legal experts, covering ~293K sentence pairs that include obligations, entitlements, and prohibitions extracted from lease agreements. Using this dataset, we train a pairwise importance ranker and propose a pipeline-based extractive summarization system that generates a party-specific contract summary.
2021
- (Hendrycks et al., 2021) ⇒ Dan Hendrycks, Collin Burns, Anya Chen, and Spencer Ball (2021). "CUAD: An expert-annotated nlp dataset for legal contract review". In: arXiv preprint, arXiv:2103.06268.
- QUOTE: Many specialized domains remain untouched by deep learning, as large labeled datasets require expensive expert annotators. We address this bottleneck within the legal domain by introducing the Contract Understanding Atticus Dataset (CUAD), a new dataset for legal contract review. CUAD was created with dozens of legal experts from The Atticus Project and consists of over 13,000 annotations(...)
2019
- (Manor & Li, 2019) ⇒ Laura Manor, and Junyi Jessy Li (2019). "Plain English summarization of contracts". In: Proceedings of the Natural Legal Language Processing Workshop 2019.
- QUOTE: Unilateral legal contracts, such as terms of service, play a substantial role in modern digital life. However, few 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.