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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.
QuillBot is a software developed in 2017 that uses artificial intelligence to rewrite and ... Text is available under the Creative Commons Attribution ...
Correcting issues – step 2) Read source information, preferably taking notes to extract essential points, and write a summary in your own words, thereby producing an acceptable version. In the 1930s a Works Progress Administration (WPA) project, called Federal Writers' Project, was conducted to capture the history record of people born into ...
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.
These models are so fluent in generating text that human experts cannot identify if an example was human-authored or machine-generated. [9] Transformer-based paraphrase generation relies on autoencoding, autoregressive, or sequence-to-sequence methods. Autoencoder models predict word replacement candidates with a one-hot distribution over the ...
Making reading an active, observable process can be very beneficial to struggling readers. A good reader interacts with the text in order to develop an understanding of the information before them. Some good reader strategies are predicting, connecting, inferring, summarizing, analyzing and critiquing.