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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.
In Skeptic, computer programmer Peter Kassan compares the book favorably with "histrionic" works such as Life 3.0, You Look Like a Thing and I Love You, and The Age of Spiritual Machines. Kassan calls the book "the most intelligent book on the subject" and praises Mitchell for being "measured, cautious, and often skeptical", unlike "most active ...
NotebookLM can generate summaries, explanations, and answers based on content uploaded by users. It also includes "Audio Overviews," which summarize documents in a conversational, podcast-like format. [7] [1] In addition to text files, NotebookLM can process PDFs, Google Docs, websites, and Google Slides. [7]
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 ...
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.
The AI technology is designed to identify hidden connections and links between research topics. [14] Like the previously cited search engines, Semantic Scholar also exploits graph structures, which include the Microsoft Academic Knowledge Graph , Springer Nature's SciGraph , and the Semantic Scholar Corpus (originally a 45 million papers corpus ...
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