  Text simplification reduces the language complexity of professional content
for accessibility purposes. End-to-end neural network models have been widely
adopted to directly generate the simplified version of input text, usually
functioning as a blackbox. We show that text simplification can be decomposed
into a compact pipeline of tasks to ensure the transparency and explainability
of the process. The first two steps in this pipeline are often neglected: 1) to
predict whether a given piece of text needs to be simplified, and 2) if yes, to
identify complex parts of the text. The two tasks can be solved separately
using either lexical or deep learning methods, or solved jointly. Simply
applying explainable complexity prediction as a preliminary step, the
out-of-sample text simplification performance of the state-of-the-art,
black-box simplification models can be improved by a large margin.
