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We have implemented a core summarizer of scientific articles written in Spanish, with the following components: a tokenizer, a grammar checker, a clarity checker, a cohesion-coherence checker, a common-topic extractor and an output formatter.
IT WILL WORK WITH A JAVA DEVELOPMENT KIT 1.7 ONLY !!!
This program is a data-miner and a knowledge-miner. It does exactly the opposite of what the text summarizers do. A textsummarizer produces a shortened text given some text as an input. An inverse summarizer takes the shortened input, a similar or a same text and does the process in reverse. This results in an expanded text.
It can be used with any text or notes that have the knowledge gaps. It is a great aid to any creative work
and it simply pin-points to data that may be of some relevance.
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Auto summarization provides a concise summary for a document.
Auto summarization provides a concise summary for a document. In this I present a Statistical approach to addressing the text generation problem in domain-independent, single-document summarization.
My thesis Includes salton’s vector space model which divides the sentences into categories which can also be used for summarizing the contents in WebPages.
The summarizer initially breaks the entire document into sentences based on the separators.
The Second step is that the unnecessary words are removed from the document.
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