Abstractive summarization is a technique in natural language processing that generates a concise summary of a text by creating new sentences. Unlike extractive summarization, which pulls sentences directly from the original text, abstractive summarization rephrases and condenses the information, often using different words and structures. This approach aims to capture the main ideas while providing a more coherent and readable summary. This method is particularly useful for applications like news articles, research papers, and social media posts, where users seek quick insights without reading the entire content. By leveraging advanced algorithms and machine learning models, abstractive summarization can enhance information accessibility and comprehension.