Seed and Grow: Augmenting Statistically Generated Summary Sentences using Schematic Word Patterns

Stephen Wan, Robert Dale, Mark Dras, Cécile Paris

Abstract: We examine the problem of content selection in statistical novel sentence generation. Our approach models the processes performed by professional editors when incorporating material from additional sentences to support some initially chosen key summary sentence, a process we refer to as Sentence Augmentation. We propose and evaluate a method called "Seed and Grow" for selecting such auxiliary information. Additionally, we argue that this can be performed using schemata, as represented by word-pair co-occurrences, and demonstrate its use in statistical summary sentence generation. Evaluation results are supportive, indicating that a schemata model significantly improves over the baseline.