  Scientific publications are the primary means to communicate research
discoveries, where the writing quality is of crucial importance. However, prior
work studying the human editing process in this domain mainly focused on the
abstract or introduction sections, resulting in an incomplete picture. In this
work, we provide a complete computational framework for studying text revision
in scientific writing. We first introduce arXivEdits, a new annotated corpus of
751 full papers from arXiv with gold sentence alignment across their multiple
versions of revision, as well as fine-grained span-level edits and their
underlying intentions for 1,000 sentence pairs. It supports our data-driven
analysis to unveil the common strategies practiced by researchers for revising
their papers. To scale up the analysis, we also develop automatic methods to
extract revision at document-, sentence-, and word-levels. A neural CRF
sentence alignment model trained on our corpus achieves 93.8 F1, enabling the
reliable matching of sentences between different versions. We formulate the
edit extraction task as a span alignment problem, and our proposed method
extracts more fine-grained and explainable edits, compared to the commonly used
diff algorithm. An intention classifier trained on our dataset achieves 78.9 F1
on the fine-grained intent classification task. Our data and system are
released at tiny.one/arxivedits.
