arXivEdits: Understanding the Human Revision Process in Scientific Writing

• URL: http://arxiv.org/abs/2210.15067v1
• Date: Wed, 26 Oct 2022 22:50:24 GMT
• Title: arXivEdits: Understanding the Human Revision Process in Scientific Writing
• Authors: Chao Jiang and Wei Xu and Samuel Stevens
• Abstract summary: 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. It supports our data-driven analysis to unveil the common strategies practiced by researchers for revising their papers.
• Score: 17.63505461444103
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

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