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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.
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Semantic Scholar is free to use and unlike similar search engines (i.e. Google Scholar) does not search for material that is behind a paywall. [ 5 ] [ citation needed ] One study compared the index scope of Semantic Scholar to Google Scholar, and found that for the papers cited by secondary studies in computer science, the two indices had ...
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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 ...
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