  Readability refers to how easily a reader can understand a written text.
Several factors affect the readability level, such as the complexity of the
text, its subject matter, and the reader's background knowledge. Generating
summaries based on different readability levels is critical for enabling
knowledge consumption by diverse audiences. However, current text generation
approaches lack refined control, resulting in texts that are not customized to
readers' proficiency levels. In this work, we bridge this gap and study
techniques to generate summaries at specified readability levels. Unlike
previous methods that focus on a specific readability level (e.g., lay
summarization), we generate summaries with fine-grained control over their
readability. We develop three text generation techniques for controlling
readability: (1) instruction-based readability control, (2) reinforcement
learning to minimize the gap between requested and observed readability and (3)
a decoding approach that uses lookahead to estimate the readability of upcoming
decoding steps. We show that our generation methods significantly improve
readability control on news summarization (CNN/DM dataset), as measured by
various readability metrics and human judgement, establishing strong baselines
for controllable readability in summarization.

Computation and Language;Artificial Intelligence;Machine Learning