  Text simplification (TS) systems rewrite text to make it more readable while
preserving its content. However, what makes a text easy to read depends on the
intended readers. Recent work has shown that pre-trained language models can
simplify text using a wealth of techniques to control output simplicity,
ranging from specifying only the desired reading grade level, to directly
specifying low-level edit operations. Yet it remains unclear how to set these
control parameters in practice. Existing approaches set them at the corpus
level, disregarding the complexity of individual inputs and considering only
one level of output complexity. In this work, we conduct an empirical study to
understand how different control mechanisms impact the adequacy and simplicity
of text simplification systems. Based on these insights, we introduce a simple
method that predicts the edit operations required for simplifying a text for a
specific grade level on an instance-per-instance basis. This approach improves
the quality of the simplified outputs over corpus-level search-based
heuristics.
