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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 2006 Google launched a beta release spreadsheet web application, this is currently known as Google Sheets and one of the applications provided in Google Drive. [16] A spreadsheet consists of a table of cells arranged into rows and columns and referred to by the X and Y locations. X locations, the columns, are normally represented by letters ...
Google Sheets is a spreadsheet application and part of the free, web-based Google Docs Editors suite offered by Google. Google Sheets is available as a web application; a mobile app for: Android, iOS, and as a desktop application on Google's ChromeOS. The app is compatible with Microsoft Excel file formats. [5]
Google Docs is an online word processor and part of the free, web-based Google Docs Editors suite offered by Google.Google Docs is accessible via a web browser as a web-based application and is also available as a mobile app on Android and iOS and as a desktop application on Google's ChromeOS.
AppSheet is a no-code development platform for application software, which allows users to create mobile, tablet, and web applications.It allows using data sources like Google Drive, DropBox, Office 365, and other cloud-based spreadsheet and database platforms.
An executive summary (or management summary, sometimes also called speed read) is a short document or section of a document produced for business purposes. It summarizes a longer report or proposal or a group of related reports in such a way that readers can rapidly become acquainted with a large body of material without having to read it all.
The graphic is intended to summarize or be an exemplar for the main thrust of the article. It is not intended to be as exhaustive a summary as the text abstract, rather it is supposed to indicate the type, scope, and technical coverage of the article at a glance.
Similarly, an image model prompted with the text "a photo of a CEO" might disproportionately generate images of white male CEOs, [128] if trained on a racially biased data set. A number of methods for mitigating bias have been attempted, such as altering input prompts [ 129 ] and reweighting training data.