Text Summarization

Text summarization is a natural language processing (NLP) technology that automatically extracts important information from long documents or multiple sources and generates a concise summary. In an era of information overload, it is widely used to efficiently grasp and share vast amounts of textual data. Text summarization primarily uses two approaches: • Extractive Summarization A method that pulls key sentences or phrases directly from the source text to create a summary • Abstractive Summarization A method that understands the content and generates a summary in new expressions, enabling paraphrasing and contextual reconstruction In recent years, abstractive summarization powered by large language models (LLMs) has achieved high accuracy, and has seen widespread adoption in use cases such as news article summarization, meeting minute generation, and organizing social media content. Text summarization is an important AI technology that helps people quickly grasp the essence of information, facilitating efficient decision-making and knowledge sharing.