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  2. Comparison of documentation generators - Wikipedia

    en.wikipedia.org/wiki/Comparison_of...

    Text C/C++, C#, D, IDL, Fortran, Java, PHP, Python Any 1997/10/26 1.9.1 GPL Epydoc: Edward Loper Text Python Any 2002/01/— 3.0 (2008) MIT: fpdoc (Free Pascal Documentation Generator) Sebastian Guenther and Free Pascal Core Text (Object)Pascal/Delphi FPC tier 1 targets 2005 3.2.2 GPL reusable parts are GPL with static linking exception Haddock

  3. 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 .

  4. Computational creativity - Wikipedia

    en.wikipedia.org/wiki/Computational_creativity

    Edmond de Belamy, an artwork generated by a generative adversarial network. Computational creativity (also known as artificial creativity, mechanical creativity, creative computing or creative computation) is a multidisciplinary endeavour that is located at the intersection of the fields of artificial intelligence, cognitive psychology, philosophy, and the arts (e.g., computational art as part ...

  5. Document automation - Wikipedia

    en.wikipedia.org/wiki/Document_automation

    Document automation (also known as document assembly) is the design of systems and workflows that assist in the creation of electronic documents. These include logic-based systems that use segments of pre-existing text and/or data to assemble a new document.

  6. Generative artificial intelligence - Wikipedia

    en.wikipedia.org/wiki/Generative_artificial...

    Generative artificial intelligence (generative AI, GenAI, [1] or GAI) is a subset of artificial intelligence that uses generative models to produce text, images, videos, or other forms of data.

  7. Automatic summarization - Wikipedia

    en.wikipedia.org/wiki/Automatic_summarization

    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.

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