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  2. Automatic summarization - Wikipedia

    en.wikipedia.org/wiki/Automatic_summarization

    An example of a summarization problem is document summarization, which attempts to automatically produce an abstract from a given document. Sometimes one might be interested in generating a summary from a single source document, while others can use multiple source documents (for example, a cluster of articles on the

  3. Wordtune - Wikipedia

    en.wikipedia.org/wiki/Wordtune

    On November 14, 2021, AI21 released Wordtune Read — an AI-powered Chrome extension and standalone app designed to process large amounts of written text from websites, documents, or YouTube videos, and summarize them into short and easily digestible pieces of text.

  4. Multi-document summarization - Wikipedia

    en.wikipedia.org/wiki/Multi-document_summarization

    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.

  5. Microsoft Copilot - Wikipedia

    en.wikipedia.org/wiki/Microsoft_Copilot

    [55] [98] The company also states that Copilot is able to create PowerPoint presentations that summarize information from user-selected Word documents and Excel spreadsheets, or from user prompts. [ 98 ] [ 100 ] Additionally, this tool can adjust text formatting, animation timing, and presentation style and length based on user prompts ...

  6. Facebook is reportedly developing AI to summarize news - AOL

    www.aol.com/facebook-reportedly-developing-ai...

    According to a report from BuzzFeed News, Facebook is testing an AI-powered tool called TL;DR (Too Long; Didn’t Read) to summarize news pieces, so you don’t even have to click through to read ...

  7. Document AI - Wikipedia

    en.wikipedia.org/wiki/Document_ai

    Document AI combines text data, which has a time dimension, with other types of data, such as the position of an address in a business letter, which is spatial. Historically in machine learning spatial data was analyzed using a convolutional neural network , and temporal data using a recurrent neural network .

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