TF*IDF (term frequency times inverse document frequency) is a common metric used to automatically discover keywords in documents for use in classification and other text processing applications. We are interested in determining whether these measures can help in determining the most relevant sentences for summarization and classification purposes. However, there are many ways to define TF*IDF, and to date no attempt to relatively—and systematically—gauge the value of these different forms has been performed. We investigate a comprehensive family of 112 TF*IDF measures (corresponding to an a priori estimate of 20 degrees of freedom among these measures) applied to 3000 CNN articles belonging in 12 classes such as Business, Sport, and World. The assumption is that at least some sets of these measures must be effective for document summarization and classification. The goal is to identify the summaries provided by TF*IDF measures that best match human generated summaries as well as find effective TF*IDF definitions for classification purposes.