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  2. Automatic summarization - Wikipedia

    en.wikipedia.org/wiki/Automatic_summarization

    For example, the set cover problem is a special case of submodular optimization, since the set cover function is submodular. The set cover function attempts to find a subset of objects which cover a given set of concepts. For example, in document summarization, one would like the summary to cover all important and relevant concepts in the document.

  3. TL;DR: This AI summarizes research papers so you don ... - AOL

    www.aol.com/tl-dr-ai-summarizes-research...

    Thankfully, researchers at the Allen Institute for Artificial Intelligence have developed a new model to summarize text from scientific papers, and present it in a few sentences in the form of TL ...

  4. Semantic Scholar - Wikipedia

    en.wikipedia.org/wiki/Semantic_Scholar

    Semantic Scholar is a research tool for scientific literature. It is developed at the Allen Institute for AI and was publicly released in November 2015. [2] Semantic Scholar uses modern techniques in natural language processing to support the research process, for example by providing automatically generated summaries of scholarly papers. [3]

  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. Executive summary - Wikipedia

    en.wikipedia.org/wiki/Executive_summary

    An executive summary (or management summary, sometimes also called speed read) is a short document or section of a document produced for business purposes. It summarizes a longer report or proposal or a group of related reports in such a way that readers can rapidly become acquainted with a large body of material without having to read it all.

  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.

  8. 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.

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