  Much of modern-day text simplification research focuses on sentence-level
simplification, transforming original, more complex sentences into simplified
versions. However, adding content can often be useful when difficult concepts
and reasoning need to be explained. In this work, we present the first
data-driven study of content addition in text simplification, which we call
elaborative simplification. We introduce a new annotated dataset of 1.3K
instances of elaborative simplification in the Newsela corpus, and analyze how
entities, ideas, and concepts are elaborated through the lens of contextual
specificity. We establish baselines for elaboration generation using
large-scale pre-trained language models, and demonstrate that considering
contextual specificity during generation can improve performance. Our results
illustrate the complexities of elaborative simplification, suggesting many
interesting directions for future work.
