CodifiedCant: Enhancing Legal Document Accessibility Using NLP and Longformer for Secure and Efficient Compliance

CodifiedCant is a new idea that employs Natural Language Processing to simplify company guidelines and legal documents. Legal texts are extensive, complicated and hard for non-experts to understand. To tackle the above problem, this research incorporates the Longformer model because it functions as a transformer-based deep learning system designed to work effectively with extensive legal documents. Longformer enables the system to handle extensive documents by keeping better track of context, which results in transforming complex legal text into easily readable formats. To enhance the search and retrieval speed, this research investigates the nuances of transforming unstructured data, like tabular data from PDFs, to vectors. This revolution supports quicker, cognisant semantic routing inside the document. Further, it assists in data arrangement and detection across massive sources of legitimate and business information. Data security is also a major priority for the platform, which utilizes network encryption to protect data and privacy. CodifiedCant is a scalable, secure and intelligent solution for better employee access to legal news, greater company transparency and reinforces better compliance in the organization. Table extraction and document simplification performance of the model are validated on Cornell LII and Kaggle evaluation datasets, respectively. CodifiedCant associates the variance relating to legitimate terminology and user knowledge.

@article{J2025,
  title     = {CodifiedCant: Enhancing Legal Document Accessibility Using NLP and Longformer for Secure and Efficient Compliance},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {5},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Jayapradha J and Su-Cheng Haw and Naveen Palanichamy and Nilanjana Bhattacharya and Aayushi Agarwal and Senthil Kumar T},
  doi       = {10.14569/IJACSA.2025.0160588},
  url       = {https://doi.org/10.14569/IJACSA.2025.0160588}
}