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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. Paraphrasing (computational linguistics) - Wikipedia

    en.wikipedia.org/wiki/Paraphrasing...

    Paraphrase or paraphrasing in computational linguistics is the natural language processing task of detecting and generating paraphrases. Applications of paraphrasing are varied including information retrieval, question answering , text summarization , and plagiarism detection . [ 1 ]

  4. ElevenLabs - Wikipedia

    en.wikipedia.org/wiki/ElevenLabs

    ElevenLabs is primarily known for its browser-based, AI-assisted text-to-speech software, Speech Synthesis, which can produce lifelike speech by synthesizing vocal emotion and intonation. [10] The company states that its models are trained to interpret the context in the text, and adjust the intonation and pacing accordingly. [ 11 ]

  5. GPT-2 - Wikipedia

    en.wikipedia.org/wiki/GPT-2

    GPT-2's flexibility was described as "impressive" by The Verge; specifically, its ability to translate text between languages, summarize long articles, and answer trivia questions were noted. [ 17 ] A study by the University of Amsterdam employing a modified Turing test found that at least in some scenarios, participants were unable to ...

  6. Praat - Wikipedia

    en.wikipedia.org/wiki/Praat

    Praat (/ p r ɑː t / PRAHT, Dutch: ⓘ; transl. "Talk") is a free, open-source computer software package widely used for speech analysis and synthesis in phonetics [4] and other fields of linguistics. It was designed and continues to be developed by Paul Boersma and David Weenink at the University of Amsterdam. [4]

  7. Generative pre-trained transformer - Wikipedia

    en.wikipedia.org/wiki/Generative_pre-trained...

    Generative pretraining (GP) was a long-established concept in machine learning applications. [16] [17] It was originally used as a form of semi-supervised learning, as the model is trained first on an unlabelled dataset (pretraining step) by learning to generate datapoints in the dataset, and then it is trained to classify a labelled dataset.

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