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  2. 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.

  3. Grammarly - Wikipedia

    en.wikipedia.org/wiki/Grammarly

    Documents whose contents have been corrected via Grammarly have occasionally been accused by detection engines such as Turnitin of being partially or entirely AI-generated. [37] Schools are struggling to develop rules about its use that are consistent and fair, with some teachers recommending Grammarly to all of their students and others ...

  4. FreeTTS - Wikipedia

    en.wikipedia.org/wiki/FreeTTS

    Gnopernicus uses these in a number of places: to know when text should and should not be interrupted, to better concatenate speech, and to sequence speech in different voices. Benchmarks conducted by Sun in 2002 on Solaris showed that FreeTTS ran two to three times faster than Flite at the time.

  5. 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.

  6. Grammar checker - Wikipedia

    en.wikipedia.org/wiki/Grammar_checker

    Grammar checkers are most often implemented as a feature of a larger program, such as a word processor, but are also available as a stand-alone application that can be activated from within programs that work with editable text. The implementation of a grammar checker makes use of natural language processing. [1] [2]

  7. Otter.ai - Wikipedia

    en.wikipedia.org/wiki/Otter.ai

    To develop its speech transcription technology, the company says it combined deep machine learning using millions of hours of audio recordings, which were analyzed to train the software and improve the transcription capabilities. The company says that it uses proprietary algorithms to scour the web for these usable audio segments.

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