Text summarization is a sub-discipline of natural language processing (NLP) – the branch of computer science and artificial intelligence that deals with the communication between humans and machines – and a method of extracting the most important information from one or more text documents to create a shortened text output.
Text summarization is used to reduce or condense a body of text into a summary of the important points or ideas, while still conveying the basic focus of the original text. It is most often used in business and academia; the output of text summarization is often leveraged to power efficient information retrieval and automated text summarization systems, as well as to assist with research.
Text summarization techniques can be both automatic, which uses algorithms that summarize a text document, and human-based, which uses human input to select the most important information from a document to be summarized. Automated methods are widely used for summarizing long and complex documents or clusters of documents while manual summarization techniques are best used for smaller documents.
Common automation techniques include natural language processing and statistical analysis which evaluates the frequency of specific words or phrases. Keyword extraction is another example; it is a method for selecting key words and phrases as the basis for a shortened text by assigning importance or weight to each keyword.
Text summarization can be beneficial to improve customer satisfaction by providing shorter page loading times, making research easier, and streamlining the summarization process. With the correct use of natural language processing and statistical analysis text summarization can be an incredibly powerful and efficient tool.
