ParaRev: Building a dataset for Scientific Paragraph Revision annotated with revision instruction

TL;DR

This paper introduces ParaRev, a dataset for scientific paragraph revision guided by detailed instructions, demonstrating that instruction-guided revisions outperform general methods across models and metrics.

Contribution

The paper presents the first dataset of scientifically revised paragraphs with detailed instructions, shifting focus from sentence to paragraph-level revision for more meaningful improvements.

Findings

Instruction-guided revisions improve quality significantly.

Paragraph-level revision captures broader context.

Dataset enables better automated scientific editing.

Abstract

Revision is a crucial step in scientific writing, where authors refine their work to improve clarity, structure, and academic quality. Existing approaches to automated writing assistance often focus on sentence-level revisions, which fail to capture the broader context needed for effective modification. In this paper, we explore the impact of shifting from sentence-level to paragraph-level scope for the task of scientific text revision. The paragraph level definition of the task allows for more meaningful changes, and is guided by detailed revision instructions rather than general ones. To support this task, we introduce ParaRev, the first dataset of revised scientific paragraphs with an evaluation subset manually annotated with revision instructions. Our experiments demonstrate that using detailed instructions significantly improves the quality of automated revisions compared to general approaches, no matter the model or the metric considered.