1. A document summarization system including components stored on a computer storage medium for execution by a computing device, comprising: a preprocessing component that preprocesses one or more documents identifying words and sentences; an analysis component scoring only a first occurrence of individual words in a document using a word position component based on word position information indicative of a position of the words relative to the beginning of the one or more documents and using a frequency component based on frequency information indicative of a frequency of occurrence of the individual words in the one or more documents, the word position component increasing as the word position for a given word is closer to the beginning of a document in which the given word occurs and the frequency component for the given word increasing with frequency of occurrence of the given word within the document; and a generation component including a stack decoder that has stacks of possible summaries of different lengths, each stack having one or more summaries of the same length, the generation component selecting sentences from the one or more documents for addition to the possible summaries to form a summary based on the scores of the individual words in the selected sentences by generating summary hypotheses each of which include a set of selected sentences, the sentences being selected using an optimization scheme that directly maximizes a sum of the scores of the individual words in the selected sentences up to a word length threshold indicative of a maximum number of words to be included in the sum of the scores, the summary comprising the summary hypothesis with a highest sum of the scores of the individual words in the summary hypothesis.
1. A document summarization system including components stored on a computer storage medium for execution by a computing device, comprising: a preprocessing component that preprocesses one or more documents identifying words and sentences; an analysis component scoring only a first occurrence of individual words in a document using a word position component based on word position information indicative of a position of the words relative to the beginning of the one or more documents and using a frequency component based on frequency information indicative of a frequency of occurrence of the individual words in the one or more documents, the word position component increasing as the word position for a given word is closer to the beginning of a document in which the given word occurs and the frequency component for the given word increasing with frequency of occurrence of the given word within the document; and a generation component including a stack decoder that has stacks of possible summaries of different lengths, each stack having one or more summaries of the same length, the generation component selecting sentences from the one or more documents for addition to the possible summaries to form a summary based on the scores of the individual words in the selected sentences by generating summary hypotheses each of which include a set of selected sentences, the sentences being selected using an optimization scheme that directly maximizes a sum of the scores of the individual words in the selected sentences up to a word length threshold indicative of a maximum number of words to be included in the sum of the scores, the summary comprising the summary hypothesis with a highest sum of the scores of the individual words in the summary hypothesis. 3. The system of claim 1 wherein the analysis component comprises: a model trained using a discriminative machine learning method, using features that use the word frequency and word position information.