1 Since the business of extractive summarization, such as one we are concerned with here, is about ranking sentences according to how useful/important they are as part of summary, we will consider here a particular ranking scheme based on the probability of a sentence being part of summary under a given DOV, i.e., where y denotes a given sentence, and stands for a DOV, an array of observed vote counts for sentences in the text; v 1 refers to the count of votes for a sentence at the text initial position, v 2 to that for a sentence occurring at the second place, etc.
given a four sentence long text, if we have three people in favor of a lead sentence, two in favor  Now suppose that each sentence y i (i.e., a sentence at the i-th place in the order of appearance) is associated with what we might call a prior preference factor θ i , representing how much a sentence at a particular position is favored as part of a summary in general.