SPoT: A Trainable Sentence Planner 
 Sentence planning is a set of inter-related but distinct tasks, one of which is sentence scoping , i.e. the choice of syntactic structure for elementary speech acts and the decision of how to combine them into one or more sentences . In this paper, we present SPoT , a sentence planner , and a new methodology for automatically training SPoT on the basis of feedback provided by human judges . We reconceptualize the task into two distinct phases. First, a very simple, randomized sentence-plan-generator (SPG) generates a potentially large list of possible sentence plans for a given text-plan input . Second, the sentence-plan-ranker (SPR) ranks the list of output sentence plans , and then selects the top-ranked plan . The SPR uses ranking rules automatically learned from training data . We show that the trained SPR learns to select a sentence plan whose rating on average is only 5% worse than the top human-ranked sentence plan . 