Automatic summarization
computer-based method for shortening a text
Automatic summarization is the process of shortening a set of data computationally, to create a subset (a summary) that represents the most important or relevant information within the original content. Artificial intelligence (AI) algorithms are commonly developed and employed to achieve this, specialized for different types of data.
Text summarization is usually implemented by natural language processing methods, designed to locate the most informative sentences in a given document. On the other hand, visual content can be summarized using computer vision algorithms. Image summarization is the subject of ongoing research; existing approaches typically attempt to display the most representative images from a given image collection, or generate a video that only includes the most important content from the entire collection. Video summarization algorithms identify and extract from the original video content the most important frames (key-frames), and/or the most important video segments (key-shots), normally in a temporally ordered fashion. Video summaries simply retain a carefully selected subset of the original video frames and, therefore, are not identical to the output of video synopsis algorithms, where new video frames are being synthesized based on the original video content.
Approaches
There are two general approaches to automatic summarization: extraction and abstraction.
Extraction-based summarization
Here, content is extracted from the original data, but the extracted content is not modified in any way. Examples of extracted content include key-phrases that can be used to "tag" or index a text document, or key sentences (including headings) that collectively comprise an abstract, and representative images or video segments, as stated above.
“Automatic summarization” enters the record as computer-based method for shortening a text. Crown Archives preserves that source wording while asking what Automatic, summarization and computer-based can confirm, complicate or overturn.
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“Automatic summarization” is worth following because a concise public description often conceals a longer documentary argument. Here, Automatic, summarization and computer-based provides the most credible route into that argument.
Vocabulary and entity names are the principal evidence signals here, because they determine the precision of every later search. The source revision retrieved here is dated Sep 20, 2026. The linked authority identifier is Q1394144. None of the 0 selected statements returned an explicit reference.
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This entry incorporates text from “Automatic summarization” on English Wikipedia. Contributors are listed in the page history. Text is available under the Creative Commons Attribution-ShareAlike 4.0 License. Selected authority identifiers and statements are retrieved from Wikidata under CC0; their references and qualifiers remain part of the verification path.