Paper Contents
Abstract
This project focuses on developing a Natural Language Processing (NLP) system designed to translate and summarize news articles. The aim is to create a tool that can efficiently handle multiple languages, providing concise summaries of lengthy news content. The system leverages state-of-theart NLP models for translation, ensuring high accuracy and contextual relevance. It also integrates advanced summarization techniques to distill essential information, making news more accessible and easier to understand. This project has significant implications for enhancing information dissemination and accessibility in a globalized world, where timely and clear communication is crucial. This project develops a Natural Language Processing (NLP) system for translating and summarizing news articles. Utilizing advanced models like neural machine translation (NMT) and transformer-based architectures (e.g., BERT, GPT), the system ensures accurate, contextually relevant translations. It employs both extractive and abstractive summarization techniques to provide concise summaries, helping users stay informed without reading lengthy articles. The tool is trained on diverse datasets and features a user-friendly interface for easy access. Its applications span news media, academia, business, and international relations, enhancing global communication .
Copyright
Copyright © 2024 Ramcharan. This is an open access article distributed under the Creative Commons Attribution License.
