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An example of a summarization problem is document summarization, which attempts to automatically produce an abstract from a given document. Sometimes one might be interested in generating a summary from a single source document, while others can use multiple source documents (for example, a cluster of articles on the same topic).
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.
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]
iA Writer is designed to focus solely on writing, offering a minimalist interface with features like distraction-free [4] mode and syntax highlighting. [5] It uses "writing typography," a concept emphasizing legibility with custom monospaced and duospaced fonts [6] derived from IBM Plex Mono. [7]
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To identify AI-generated images and ensure appropriate usage. To help and keep track of AI-using editors who may not realize the deficiencies of AI as a writing tool. The purpose of this project is not to restrict or ban the use of AI in articles, but to verify that its output is acceptable and constructive, and to fix or remove it otherwise.
Document AI combines text data, which has a time dimension, with other types of data, such as the position of an address in a business letter, which is spatial. Historically in machine learning spatial data was analyzed using a convolutional neural network , and temporal data using a recurrent neural network .
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