Text summarization is the computational process of generating a concise and coherent representation of a source text or collection of texts, designed to convey the main information and meaning while substantially reducing length. As a significant research area within Natural Language Processing (NLP), it investigates methodologies for identifying salient content (e.g., through extraction or abstraction), developing algorithms for producing fluent and informative summaries, and establishing metrics for evaluating summary quality, relevance, and factual consistency. Its academic significance lies in addressing challenges related to information overload, enabling efficient information access, and supporting various downstream applications requiring automated content compression and synthesis.