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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 ...
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.
Semantic Scholar provides a one-sentence summary of scientific literature. One of its aims was to address the challenge of reading numerous titles and lengthy abstracts on mobile devices. [ 7 ] It also seeks to ensure that the three million scientific papers published yearly reach readers, since it is estimated that only half of this literature ...
A paper published by researchers at Amazon Web Services AI Labs found that over 57% of sentences from a sample of over 6 billion sentences from Common Crawl, a snapshot of web pages, were machine translated. Many of these automated translations were seen as lower quality, especially for sentences that were translated across at least three ...
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.
Otter.ai was founded as AISense in 2016 by Sam Liang and Yun Fu, two computer science engineers with a long history of working with artificial intelligence. [ 2 ] [ 3 ] In January 2018, the company announced a partnership with Zoom Video Communications to transcribe video meetings post-conference. [ 4 ]
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