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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. Undetectable.ai - Wikipedia

    en.wikipedia.org/wiki/Undetectable.ai

    In July 2023, researchers from Magna Græcia University tested Undetectable.ai against generative-text and plagiarism detection software. They found that texts processed through Undetectable.ai were significantly harder to detect as AI-generated.

  4. QuillBot - Wikipedia

    en.wikipedia.org/wiki/QuillBot

    According to a 30 under 30 listing on Forbes QuillBot has a user base that includes both free and premium subscribers. The listing also states that in August 2023, QuillBot was acquired by Course Hero. [5]

  5. Artificial intelligence content detection - Wikipedia

    en.wikipedia.org/wiki/Artificial_intelligence...

    Artificial intelligence detection software aims to determine whether some content (text, image, video or audio) was generated using artificial intelligence (AI).. However, the reliability of such software is a topic of debate, [1] and there are concerns about the potential misapplication of AI detection software by educators.

  6. SCIgen - Wikipedia

    en.wikipedia.org/wiki/SCIgen

    SCIgen is a paper generator that uses context-free grammar to randomly generate nonsense in the form of computer science research papers. Its original data source was a collection of computer science papers downloaded from CiteSeer. All elements of the papers are formed, including graphs, diagrams, and citations.

  7. Wikipedia : Using neural network language models on Wikipedia

    en.wikipedia.org/wiki/Wikipedia:Using_neural...

    Summarizing a reliable source. This is inherently risky, due to the likelihood of an LLM introducing original research or bias that was not present in the source, as well as the risk that the summary may be an excessively close paraphrase, which would constitute plagiarism. You must proactively ensure such a summary complies with all policies.

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