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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.
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.
To summarize this into a remark distinguishing libre (freedom) software from gratis (zero price) software, the Free Software Foundation says: "Free software is a matter of liberty, not price. To understand the concept, you should think of 'free' as in ' free speech ', not as in 'free beer ' ". [ 22 ] (
Summary style keeps the reader from being overwhelmed by too much information up front, by summarizing main points and going into more details on particular points (subtopics) in separate articles. What constitutes "too long" varies by situation, but generally 50 kilobytes of readable prose (8,000 words) is the starting point at which articles ...
These summaries appear in recent changes, page histories, and contributions lists, unless you provide your own edit summary. Auto edit summaries exist for several actions, if the user fails to provide an edit summary: Blanking a page; Replacing entire page with other text, removing more than 90% of the content of a page
This template is intended for articles which should be (but are not) summarized in another article (usually a 'parent' article; i.e. they are a subarticle of the 'parent').
This page was last edited on 13 October 2021, at 13:46 (UTC).; Text is available under the Creative Commons Attribution-ShareAlike 4.0 License; additional terms may apply.
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]
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