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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.
Such text compilations should also follow basic requirements for an overview text compiled by a human. The multi-document summary quality criteria are as follows: clear structure, including an outline of the main content, from which it is easy to navigate to the full text sections; text within sections is divided into meaningful paragraphs
It is a general-purpose learner and its ability to perform the various tasks was a consequence of its general ability to accurately predict the next item in a sequence, [2] [7] which enabled it to translate texts, answer questions about a topic from a text, summarize passages from a larger text, [7] and generate text output on a level sometimes ...
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. [130]
Wordtune is an AI powered reading and writing companion capable of fixing grammatical errors, understanding context and meaning, suggesting paraphrases or alternative writing tones, and generating written text based on context. [1] [2] [3] It is developed by the Israeli AI company AI21 Labs. [4] [5] [6] [7]
As explained in Wikipedia:Plot-only description of fictional works, an encyclopedia article about a work of fiction frequently includes a concise summary of the plot. The description should be thorough enough for the reader to get a sense of what happens and to fully understand the impact of the work and the context of the commentary about it.
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