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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).
The Document Understanding Conferences, [1] conducted annually by NIST, have developed sophisticated evaluation criteria for techniques accepting the multi-document summarization challenge. An ideal multi-document summarization system not only shortens the source texts, but also presents information organized around the key aspects to represent ...
Document management systems may have a rights management module that allows an administrator to give access to documents based on type to only certain people or groups of people. Document marking at the time of printing or PDF-creation is an essential element to preclude alteration or unintended use. Workflow
All students get access to Cloud resources and Azure credit. student must register at Microsoft Azure for Student [6] and verify their identity through their verified educational institutions. If an institution is not listed on the available list, the user may manually verify their student status by uploading a proof such as an ID card.
More commonly, question-answering systems can pull answers from an unstructured collection of natural language documents. Some examples of natural language document collections used for question answering systems include: a local [clarification needed] collection of reference texts; internal organization [ambiguous] documents and web pages
For document clustering, one of the most common ways to generate features for a document is to calculate the term frequencies of all its tokens. Although not perfect, these frequencies can usually provide some clues about the topic of the document. And sometimes it is also useful to weight the term frequencies by the inverse document frequencies.
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Documents are addressed in the database via a unique key that represents that document. Another defining characteristic of a document-oriented database is an API or query language to retrieve documents based on their contents. Different implementations offer different ways of organizing and/or grouping documents: Collections; Tags; Non-visible ...
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