Text Summarization

Text summarization is a task in natural language processing that aims to compress long documents into shorter, more concise versions while retaining the core information and meaning of the original text. Its goal is to produce summaries that accurately reflect the original content, enabling users to quickly grasp key information. This task encompasses both extractive and abstractive methods; the former identifies and extracts important sentences or phrases, while the latter generates new text based on the content of the original document. Text summarization has significant application value in areas such as news reporting, scientific literature, and business reports.