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New software-as-a-service and cloud computing models use artificial intelligence, machine learning and natural language processing to convert speech to text and continuously learn new phrases and accents. [3] AI transcription can, however, lead to hallucinations and other errors. [4] [5] [6] [7]
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.
Zamzar is an online file converter and compressor, created by brothers Mike and Chris Whyley in England in 2006. [1] [2] It allows users to convert files online, without downloading a software tool, and supports over 1,200 different conversion types. [3]
Sentence extraction is a technique used for automatic summarization of a text. In this shallow approach, statistical heuristics are used to identify the most salient sentences of a text. Sentence extraction is a low-cost approach compared to more knowledge-intensive deeper approaches which require additional knowledge bases such as ontologies ...
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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Scrivener (/ ˈ s k r ɪ v ən ər /) is a word-processing program and outliner designed for writers. [5] Scrivener provides a management system for documents, notes and metadata.This allows the user to organize notes, concepts, research, and whole documents for easy access and reference (documents including rich text, images, PDF, audio, video, and web pages).
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