Text Summarization
What is Text Summarization?
Text summarization in the field of artificial intelligence involves the use of algorithms and machine learning models to automatically generate brief and coherent summaries of longer texts. This task is crucial for managing information overload in today's digital age, where vast amounts of textual data are generated daily. By distilling the essential information from a document, text summarization helps users quickly grasp the main ideas without having to read the entire content. There are two main types of text summarization techniques: extractive and abstractive. Extractive summarization selects key sentences or phrases directly from the source text, while abstractive summarization generates new sentences that convey the main points, often requiring a deeper understanding of the text's context and semantics.
A process in artificial intelligence that involves creating a concise and coherent summary of a longer piece of text.
Examples
- News Aggregators: Applications like Google News use text summarization to provide concise summaries of news articles, allowing users to quickly get the gist of the latest stories without reading full articles.
- Customer Support: Companies like Zendesk employ text summarization in their customer support systems to summarize lengthy customer queries and past interactions, enabling support agents to respond more effectively and efficiently.
Additional Information
- Text summarization can significantly reduce the time required to process large volumes of information.
- It is widely used in various domains including legal, medical, and academic fields to aid in quicker decision-making and information retrieval.