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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. [8] [1] In addition to text files, NotebookLM can process PDFs, Google Docs, websites, and Google Slides. [8]
Data is typically distinguished in spatial data and time-series data, the former can be things like images, maps, graphs, etc. the latter can be e.g. stock-price or a voice recording. Document AI combines text data, which has a time dimension, with other types of data, such as the position of an address in a business letter, which is spatial.
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 ...
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.
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