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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.
The to/from and article title parameters are optional, but it is highly recommend that you fill them out so that people know why the template is there. If the page name you provide in the article title parameter does not exist, the template will display without the article name (it will still make sense, but vaguely suggest only "another article".
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.
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.
Here are just some reasons why it is worthwhile creating spoken recordings of articles. Spoken articles make Wikipedia content available to those who can understand English but cannot read it. Users can listen to Wikipedia articles while they perform tasks that preclude reading but not concentration (such as running, or housework).
Keira Knightley’s number one reason for having no more kids isn’t the pain of childbirth or the endless nights of disrupted sleep. On Monday, Dec. 9. the actress, 39, gushed about her two ...
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