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Text editing, i.e., the process of modifying or manipulating text, is acrucial step in human writing process. In this paper, we study the problem ofcontrolled text editing by natural language instruction. According to a giveninstruction that conveys the edit intention and necessary information, anoriginal draft text is required to be revised into a target text. Existingautomatically constructed datasets for this task are limited because they donot have informative natural language instruction. The informativeness requiresthe information contained in the instruction to be enough to produce therevised text. To address this limitation, we build and release WikiIns, ahigh-quality controlled text editing dataset with improved informativeness. Wefirst preprocess the Wikipedia edit history database to extract the raw data(WikiIns-Raw). Then we crowdsource high-quality validation and test sets, aswell as a small-scale training set (WikiIns-Gold). With the high-qualityannotated dataset, we further propose automatic approaches to generate alarge-scale ``silver'' training set (WikiIns-Silver). Finally, we provide someinsightful analysis on our WikiIns dataset, including the evaluation resultsand the edit intention analysis. Our analysis and the experiment results onWikiIns may assist the ongoing research on text editing. The dataset, sourcecode and annotation guideline are available athttps://github.com/CasparSwift/WikiIns.

WikiIns: A High-Quality Dataset for Controlled Text Editing by Natural Language Instruction