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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.
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.
The approach arose in the context of machine translation, [93] [94] [95] where the input and output are written sentences in two natural languages. In that work, an LSTM RNN or CNN was used as an encoder to summarize a source sentence, and the summary was decoded using a conditional RNN language model to produce the translation. [96]
Summarize awards and achievements using proper context in a later paragraph, and avoid descriptive phrases like "award-winning" to maintain a neutral point of view. Any summary of the film's critical reception should avoid synthesis, meaning it should reflect an overall consensus explicitly summarized by one or more reliable sources.
An autoencoder is a type of artificial neural network used to learn efficient codings of unlabeled data (unsupervised learning).An autoencoder learns two functions: an encoding function that transforms the input data, and a decoding function that recreates the input data from the encoded representation.
It can be expressed by numerous definitions, for example "0 for negative inputs, output equals input for non-negative inputs". The term "ramp" can also be used for other functions obtained by scaling and shifting , and the function in this article is the unit ramp function (slope 1, starting at 0).
This section should include a summary of, or be summarized in, another article. See Wikipedia:Summary style for information on how to incorporate it into this article's main text, or the main text of another article.
The step response of a system in a given initial state consists of the time evolution of its outputs when its control inputs are Heaviside step functions. In electronic engineering and control theory , step response is the time behaviour of the outputs of a general system when its inputs change from zero to one in a very short time.