The summarization module applies machine learning–based natural language processing (NLP) models to generate concise, human-readable summaries of individual regulatory documents. By automatically identifying and structuring key obligations, requirements, and contextual information, the module allows users to rapidly assess the content relevance of complex texts.

The functionality is integrated into the SGS Digicomply platform and operates directly on a repository of real regulatory documents used during the EFRA project. Access is provided through authenticated user accounts, ensuring data security and controlled demonstration environments.

Iterative refinement cycles focused on three main aspects:

Read more on Medium