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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.
An optional |better=y parameter can be added, that will insert a request that the summary be more complete. This can be used to request that regular editors of a major article summarize a subtopical article (especially after a WP:SPLIT better in the main article, per WP:SUMMARY style; this is usually used in a particular section, with the ...
The idea is that you open a long-form article of some kind and Google's AI puts together a bullet-point list of key points within the article. Click on one, and it's supposed to take you to the ...
According to a report from BuzzFeed News, Facebook is testing an AI-powered tool called TL;DR (Too Long; Didn’t Read) to summarize news pieces, so you don’t even have to click through to read ...
{{Summarize section}} – For sections that are too detailed and need to be summarized. {{ Example farm }} – For excessive use of examples. {{ Too many see alsos }} – For an indiscriminate "See also" section, most of which should be pruned or integrated into the prose.
This page in a nutshell: Avoid using large language models (LLMs) to write original content or generate references. LLMs can be used for certain tasks (like copyediting, summarization, and paraphrasing) if the editor has substantial prior experience in the intended task and rigorously scrutinizes the results before publishing them.
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