Learning to Simplify Children Stories with Limited Data

In this paper, we examine children stories and propose a text simplification system to automatically generate simpler versions of the stories and, therefore, make them easier to understand for children, especially ones with difficulty in reading comprehension. Our system learns simplifications from limited data built from a small repository of short English stories for children and can perform important simplification operations, namely splitting, dropping, reordering, and substitution. Our experiment shows that our system outperforms other systems in a variety of automatic measures as well as human judgements with regard to simplicity, grammaticality, and semantic similarity.