NLP Based Automated Text Summarization and Translation: A Comprehensive Analysis

Extractive text summarization stands as a fundamental pursuit within natural language processing, offering the capability to distil extensive textual content while preserving essential information. This research study presents a Graphical User Interface (GUI) application developed in Python using the TKinter library, designed to streamline the process of document summarization. Leveraging advanced image processing techniques, including face recognition through libraries such as OpenCV and PIL, the proposed system integrates robust security measures for user registration and login functionalities. By utilizing NLP, speed and accuracy, our system offers scalable and adaptable solution for text summarization and language translation with accuracy between 91% to 95%. By employing efficient algorithms, users can extract pivotal sentences from documents, facilitating expedited comprehension and analysis. The fusion of text summarization with secure authentication mechanisms addresses both productivity and security concerns within document management systems, culminating in a professional-grade solution tailored to contemporary information processing needs.