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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 ...
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.
Mathematica – provides built in tools for text alignment, pattern matching, clustering and semantic analysis. See Wolfram Language, the programming language of Mathematica. MATLAB offers Text Analytics Toolbox for importing text data, converting it to numeric form for use in machine and deep learning, sentiment analysis and classification ...
Computer-assisted translation is a broad and imprecise term covering a range of tools. These can include: Translation memory tools (TM tools), consisting of a database of text segments in a source language and their translations in one or more target languages. [2]
The algorithm underlying the software studied a vast pool of proper sentences in English and builds a model of proper language. The software does not analyze the text at the level of the word, but of the whole sentence. Dyslectics can have trouble choosing the right word – hence the attention to the sentence as a whole. [10]
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 ...