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The multi-document summarization task is more complex than summarizing a single document, even a long one. The difficulty arises from thematic diversity within a large set of documents. A good summarization technology aims to combine the main themes with completeness, readability, and concision.
Mail merge consists of combining mail and letters and pre-addressed envelopes or mailing labels for mass mailings from a form letter. [1]This feature is usually employed in a word processing document which contains fixed text (which is the same in each output document) and variables (which act as placeholders that are replaced by text from the data source word to word).
A merge, or merger, is the process of uniting two or more pages into a single page. It is done by copying some or all content from the source page(s) into the destination page and then replacing the source page with a redirect to the destination page. Any editor can perform a merge.
To refine your search, combine words into keyword phrases. Experiment with the examples below and compare them to the basic keywords above: • Chicago pizza • Baltimore baseball stadium • Beagle puppies. Now, rather than getting results that contain only one word, you'll get a list of sites that contain all of the words in your query.
Merge algorithm, an algorithm for combining two or more sorted lists into a single sorted one; Mail merge, the production of multiple documents from a single template form and a structured data source; Randomness merger, a function which combines several, perhaps correlated, random variables into one high-entropy random variable
Like the bag-of-words model, it models a document as a multiset of words, without word order. It is a refinement over the simple bag-of-words model, by allowing the weight of words to depend on the rest of the corpus. It was often used as a weighting factor in searches of information retrieval, text mining, and user modeling.