Text summarization in AI is a task of condensing a text document into its most crucial information, often resulting in a shorter version. There are two main techniques: extractive and abstractive summarization.
Extractive summarization involves selecting key sentences or phrases from the original text. For example, a 1000-word article about a new technology could be condensed into a 250-word summary that highlights the main points and benefits.
Abstractive summarization uses advanced machine learning models to generate a new and concise summary that retains the essence of the original text. For example, a lengthy news article could be summarized into a brief headline and a few sentences that capture the main events and details.
The goal of text summarization in AI is to provide a clear and concise representation of the original text while maintaining its meaning and context.
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Text-to-Speech Translation (TTS)
Text-to-speech (TTS) is a technology that converts written text into lifelike speech.
Natural Language Processing (NLP)
Natural Language Processing (NLP) is a branch of computer science that enables machines to interpret and comprehend human language for various tasks.
Large Language Model (LLM)
A Large Language Model (LLM) is an advanced artificial intelligence system that processes and generates human language.