1. A method in a computer system for identifying significant sentences of a document, the method comprising: providing a training set of training documents along with an indication of whether words of the training documents are important or not important as indicated by a person; for each training document, generating multiple scores for each word indicated as being important or not important using different scoring techniques to generate a technique-specific importance of the word to the training document; training a support vector machine classifier to classify words as important or not important to a document based on training data that includes the multiple scores for the words generated from different scoring techniques and the indications of whether the words are important or not important, the multiple scores for a word being a feature vector for the word, the training including finding a hyper-surface in a space represented by the feature vectors of the words that attempts to split feature vectors for words classified as important from feature vectors for words classified as not important; generating scores using the different scoring techniques for words of the document; identifying important words of the document using the trained support vector machine classifier to classify from the generated scores those words that are important to the document; calculating significance of sentences of the document based on the identified important words contained in the sentences; and storing an indication of the calculated significance of the documents.
1. A method in a computer system for identifying significant sentences of a document, the method comprising: providing a training set of training documents along with an indication of whether words of the training documents are important or not important as indicated by a person; for each training document, generating multiple scores for each word indicated as being important or not important using different scoring techniques to generate a technique-specific importance of the word to the training document; training a support vector machine classifier to classify words as important or not important to a document based on training data that includes the multiple scores for the words generated from different scoring techniques and the indications of whether the words are important or not important, the multiple scores for a word being a feature vector for the word, the training including finding a hyper-surface in a space represented by the feature vectors of the words that attempts to split feature vectors for words classified as important from feature vectors for words classified as not important; generating scores using the different scoring techniques for words of the document; identifying important words of the document using the trained support vector machine classifier to classify from the generated scores those words that are important to the document; calculating significance of sentences of the document based on the identified important words contained in the sentences; and storing an indication of the calculated significance of the documents. 4. The method of claim 1 wherein the scoring techniques are Luhn-based.