• Manual Summarization

This is the most common and traditional way of summarizing contracts; it is best suited when summarization needs close attention from a legal expert to provide detailed and tailored results. It can be undertaken immediately with the help of an in-house legal expert. This is an easy and valuable way of summarization when the company has a small or a fewer number of contracts, that can be handled internally with its own resources.

• Automatic Summarization

Automation has touched every aspect of the business – even contract management is not an exception. There are different automation tools and services available in the market to take care of various components of contract lifecycle management, starting from contract drafting to negotiations; execution to renewal, and obligation management. For automatization of the summarization of contracts, Artificial Intelligence (AI), Natural Language (NL), and Machine Learning (ML) software technologies are used. AI uses a cognitive computing technology platform to computerize the human thought process which involves self-learning systems that use data mining, pattern recognition, and natural language processing to mimic the way the human brain works. A program is designed using different algorithms to make the machine smart enough to perform various tasks by itself, based on this cognitive computing. When looking for consistency and speed, automatic summarization is the answer. It can easily handle lengthy and complicated structured and unstructured data in contracts and is trained to fetch the best-suited information.

• Hybrid Summarization

Both manual and automatic summarization methods have their own pros and cons. A manual method is desirable where the volume of contracts is manageable (i.e. smaller amounts) and requires a human eye, for such projects, it is better suited, here automation might take a little longer to complete the project as initial set-up time is generally of the same length for any size of the project whereas because of exponential effect automation is more suitable for a bigger project.

Taking the best of both—automation for consistency and workload/cost reduction, and manual oversight for completeness and accuracy—yields a blended, ‘hybrid’ approach. The focus is still on software, typically of the vendor’s own design to automate much of the abstraction process. Then, they maintain internal teams of trained legal professionals to do the manual tasks of filling in the missing data and performing quality control.