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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.
Too long; didn't read (abbreviated TL;DR and tl;dr) is a shorthand to indicate that a passage is too long to invest the time to digest it. [3] Akin to Wall of text.. The label is often used to point out excessive verbosity or to signify the presence of and location of a short summary in case the page is too long and won't otherwise be read. [4]
Perplexity AI is a conversational search engine that uses large language models (LLMs) to answer queries using sources from the web and cites links within the text response. [ 3 ] [ 4 ] Its developer, Perplexity AI, Inc., is based in San Francisco, California .
LangChain is a software framework that helps facilitate the integration of large language models (LLMs) into applications. As a language model integration framework, LangChain's use-cases largely overlap with those of language models in general, including document analysis and summarization, chatbots, and code analysis.
TL;DR or tl;dr, short for "too long; didn't read", is internet slang often used to introduce a summary of an online post or news article. [1] It is also used as an informal interjection commenting that a block of text has been ignored due to its length.
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