Urdu Language Text Summarization using Machine Learning

Abstract

Text summarization involves creating a concise version of a text while preserving its essential details and core message. This technique allows for quick understanding of lengthy articles, documents, or books, saving time and enhancing comprehension. There are two primary approaches to summarization: extractive and abstractive. Extractive summarization involves selecting key sentences or phrases from the text, while abstractive summarization generates new sentences that convey the original meaning. In the context of Urdu news articles, summarization is particularly valuable, as it enables readers with limited time or attention to grasp the main points quickly. This study explores various methods for summarizing Urdu news articles, evaluating both extractive and abstractive approaches using various datasets. To evaluate the proposed model, we use ROUGE metric which shows the significant improvement and efficiency compared with existing models. The study also highlights challenges and future directions in this field, including the complexity of Urdu sentence structures, addressing biases in content of news, and incorporating the latest development natural language processing and deep learning.