Organizations operate in a constantly changing environment. They face many disruptions that can affect their sustainability. Cyber resilience is a de facto concept on which they can rely on to ensure their sustainability. It is defined as the ability of a nation, organization, or mission or business process to anticipate, withstand, recover from, and evolve to improve capabilities in the face of adverse conditions, stresses, or attacks on the supporting cyber resources it needs to function. It is influenced by several disciplines. From these disciplines, it is necessary to extract the requirements that the organization must comply with to be able to be cyber resilient. Once the requirements have been extracted, they need to be cleaned up to eliminate duplicates, so that only a single set of requirements applicable to the organization remains. Manual extraction is tedious. We propose an approach integrating Machine Learning to automate the extraction of cyber resilience requirements from documents written in natural language. This extraction is followed by the identification of duplicates to retain single set of requirements that will be implemented for cyber resilience enhancement.