On Day 3 of 20 in our series on 'Generative AI for Contracts', lets talk about Contract Summaries.

How Generative AI can be deployed to build accurate Contract Summaries efficiently.

When it comes to Contract Summaries, there are two types-

• A legal summary of a contract typically provides a detailed analysis of the legal terms and provisions of the contract. It is written in a highly technical and legalistic language, and is primarily intended for lawyers and other legal professionals who need to understand the legal implications of the contract. The legal summary may include detailed discussions of the governing law, jurisdiction, indemnification, warranties, and other legal terms that are critical to the agreement.
• On the other hand, a commercial summary of a contract is written in a more user-friendly language that is accessible to non-legal professionals, such as business executives or stakeholders. The commercial summary focuses on the commercial terms of the agreement, such as the scope of services, pricing, payment terms, and deliverables. It may also include a summary of key business risks, obligations, and benefits, and is designed to help the business stakeholders to understand the commercial implications of the agreement.

Typically, all organizations have developed some sort of template or checklist that the summaries follow. Usually, it contains a combination of important legal and commercial considerations. And its usually 1-2 pages (so that busy executives can approve the contract on its basis). Using Generative AI to assist in population of such a summary template is a very powerful usecase. We will look briefly at how this is done, later in the article.

Before we delve into the details of how to develop these customized contract summaries, lets discuss what a summary is.

When it comes to generating summaries using generative AI models, there are two main approaches: abstractive and extractive.

Abstractive Summaries:

Abstractive summarization involves generating a summary that uses natural language to express the essence of the original text. This approach involves analyzing the text and generating a summary that captures the main ideas and themes of the original text, while also using new words and phrasing to create a more readable and engaging summary. Abstractive summarization is more challenging than extractive summarization because it requires the AI model to understand the meaning of the text and to be able to generate new language that accurately reflects that meaning.

Lets take a sneak peek at how ContractKen creates a generic contract summary:

Extractive Summaries:

Extractive summarization, on the other hand, involves generating a summary that uses exact words and phrases from the original text. This approach involves identifying the most important sentences or phrases in the original text and using them to create a condensed summary. Extractive summarization is easier to implement than abstractive summarization because it does not require the AI model to generate new language. However, extractive summarization may not capture the full meaning of the text and may result in a summary that is less engaging and readable than an abstractive summary.

Lets take a sneak peek at how ContractKen creates a specific contract terms summary:

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Now, lets discuss how custom summary templates can be automatically generated with high accuracy:

• You need a (largely) fixed template or checklist that you're looking to populate for new contract documents.
• First step is to understand all the elements in the summary in detail (by reading through the relevant portion of the contract and with the help of an expert)
• Understand the nuances of language which represent specific concepts / terms in the summary document.
• Write specific questions (as you'd ask your legal assistant) around those specific topics
• Convert those into prompts that will enable OpenAI API to get you the right answer
• However, how does the API know where to search for the specific answer (trying to scan through the entire document each time for a specific question is not a very efficient algorithm)
• Hence, we need to use embeddings. This simply means that the entire text document (each paragraph / sentence / word) is converted to mathematical representations called as vector embeddings. (Vector embeddings are a powerful tool in AI that enable computers to capture the meaning of words and phrases in a way that can be easily processed and analyzed)

Reach out to us at hello@contractken to discuss how you can leverage Generative AI to create custom summaries for your contracts