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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.
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 ...
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]
If the correct parameters are used, this template produces output identical to that of the {} template, with one important exception: By default, this Citation template uses periods (full stops) in places where the Citation template use commas by default; either type of template can use periods (full stops) or commas by using an optional parameter.
date: Full date of publication, in same format as main text of article. Or, use year. If you also have the day, use date instead. (optional) archive-url: URL of the archive location of the item, and archive-date: Date when the item was archived, in same format as main text of the article.
Such text compilations should also follow basic requirements for an overview text compiled by a human. The multi-document summary quality criteria are as follows: clear structure, including an outline of the main content, from which it is easy to navigate to the full text sections; text within sections is divided into meaningful paragraphs
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