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Abstractive summarization methods generate new text that did not exist in the original text. [12] This has been applied mainly for text. Abstractive methods build an internal semantic representation of the original content (often called a language model), and then use this representation to create a summary that is closer to what a human might express.
Multi-document summarization is an automatic procedure aimed at extraction of information from multiple texts written about the same topic. The resulting summary report allows individual users, such as professional information consumers, to quickly familiarize themselves with information contained in a large cluster of documents.
It will also work with other datasets in response to an input by the user of the AI. This AI can automate some tasks and assist the user to make a paragraph or outline from a line of text. Notion AI is an extra cost above the subscription. [38] [39] The service is powered by Anthropic's Claude [40] as well as OpenAI's GPT-4. [41]
This page in a nutshell: Avoid using large language models (LLMs) to write original content or generate references. LLMs can be used for certain tasks (like copyediting, summarization, and paraphrasing) if the editor has substantial prior experience in the intended task and rigorously scrutinizes the results before publishing them.
Otter.ai was founded as AISense in 2016 by Sam Liang and Yun Fu, two computer science engineers with a long history of working with artificial intelligence. [ 2 ] [ 3 ] In January 2018, the company announced a partnership with Zoom Video Communications to transcribe video meetings post-conference. [ 4 ]
Too long; didn't read (abbreviated TL;DR and tl;dr) is a shorthand to indicate that a passage is too long to invest the time to digest it. [3] Akin to Wall of text.. The label is often used to point out excessive verbosity or to signify the presence of and location of a short summary in case the page is too long and won't otherwise be read. [4]
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