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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.
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]
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 ...
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 ...
As with any code (including stuff you found on Stack Exchange), you should make sure you understand what it's doing before you execute it: bugs and errors can cause unintended behavior. Common sense is required; as with all programming, you should not put large chunks of code into production if you haven't tested them beforehand, don't ...
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