A Review of Text Summarization in Natural Language Processing (NLP)

This article offers a thorough examination of text summarization in Natural Language Processing (NLP), covering techniques, challenges, applications, and recent advances. It explores both traditional methods and the shift toward machine learning, particularly emphasizing transformer-based models like BERT and GPT. Addressing challenges such as ambiguity and domain-specific content, the article provides insights into evaluating summary quality.Real-world applications of text summarization across industries are highlighted, emphasizing its crucial role in information retrieval and user experience enhancement. The article delves into state-of-the-art models and recent advancements, particularly focusing on the transformative impact of transformer architectures. It also speculates on future trends and potential research directions in text summarization, underlining its evolving nature and integration with other NLP tasks.In summary, this review serves as a valuable resource for NLP researchers, practitioners, and enthusiasts, offering a comprehensive understanding of the current landscape, challenges, and promising developments in the dynamic field of text summarization.