  Iterative text revision improves text quality by fixing grammatical errors,
rephrasing for better readability or contextual appropriateness, or
reorganizing sentence structures throughout a document. Most recent research
has focused on understanding and classifying different types of edits in the
iterative revision process from human-written text instead of building accurate
and robust systems for iterative text revision. In this work, we aim to build
an end-to-end text revision system that can iteratively generate helpful edits
by explicitly detecting editable spans (where-to-edit) with their corresponding
edit intents and then instructing a revision model to revise the detected edit
spans. Leveraging datasets from other related text editing NLP tasks, combined
with the specification of editable spans, leads our system to more accurately
model the process of iterative text refinement, as evidenced by empirical
results and human evaluations. Our system significantly outperforms previous
baselines on our text revision tasks and other standard text revision tasks,
including grammatical error correction, text simplification, sentence fusion,
and style transfer. Through extensive qualitative and quantitative analysis, we
make vital connections between edit intentions and writing quality, and better
computational modeling of iterative text revisions.
