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. Here, content is extracted from the original data, but the extracted content is not modified in any way. There are two general approaches to automatic summarization: extraction and abstraction. In 2022 Google Docs released an automatic summarization feature. 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. 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. 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. On the other hand, visual content can be summarized using computer vision algorithms. Text summarization is usually implemented by natural language processing methods, designed to locate the most informative sentences in a given document.
Artificial intelligence algorithms are commonly developed and employed to achieve this, specialized for different types of data. JSTOR ( April 2022) ( Learn how and when to remove this template message)Īutomatic 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.
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