Extractive summarization techniques cannot generate document summaries shorter than a single sentence, something that is often required. An ideal summarization system would understand each document and generate an appropriate summary directly from the results of that understanding. A more practical approach to this problem results in the use of an approximation: viewing summarization as a problem analogous to statistical machine translation. The issue then becomes one of generating a target document in a more concise language from a source document in a more verbose language. This paper presents results on experiments using this approach, in which statistical models of the term selection and term ordering are jointly applied to produce summaries in a style learned from a training corpus.