Abstractive summarization is a natural language processing technique that generates a concise and coherent summary by interpreting and rephrasing the main ideas of the source text, rather than merely extracting sentences verbatim. This approach leverages advanced machine learning models, such as transformers, to understand context and semantics, enabling the creation of summaries that are closer to human-like abstraction and synthesis.