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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.
Paraphrase or paraphrasing in computational linguistics is the natural language processing task of detecting and generating paraphrases. Applications of paraphrasing are varied including information retrieval, question answering , text summarization , and plagiarism detection . [ 1 ]
A paraphrase can be introduced with verbum dicendi—a declaratory expression to signal the transition to the paraphrase. For example, in "The author states 'The signal was red,' that is, the train was not allowed to proceed," the that is signals the paraphrase that follows. A paraphrase does not need to accompany a direct quotation. [20]
It was partially released in February 2019, followed by full release of the 1.5-billion-parameter model on November 5, 2019. [ 3 ] [ 4 ] [ 5 ] GPT-2 was created as a "direct scale-up" of GPT-1 [ 6 ] with a ten-fold increase in both its parameter count and the size of its training dataset. [ 5 ]
Generative pretraining (GP) was a long-established concept in machine learning applications. [16] [17] It was originally used as a form of semi-supervised learning, as the model is trained first on an unlabelled dataset (pretraining step) by learning to generate datapoints in the dataset, and then it is trained to classify a labelled dataset.
An abstract is a brief summary of a research article, thesis, review, conference proceeding, or any in-depth analysis of a particular subject and is often used to help the reader quickly ascertain the paper's purpose. [1]