Writing a scientific article is a challenging task as it is a highly codified and specific genre, consequently proficiency in written communication is essential for effectively conveying research findings and ideas. In this article, we propose an original textual resource on the revision step of the writing process of scientific articles. This new dataset, called CASIMIR, contains the multiple revised versions of 15,646 scientific articles from OpenReview, along with their peer reviews. Pairs of consecutive versions of an article are aligned at sentence-level while keeping paragraph location information as metadata for supporting future revision studies at the discourse level. Each pair of revised sentences is enriched with automatically extracted edits and associated revision intention. To assess the initial quality on the dataset, we conducted a qualitative study of several state-of-the-art text revision approaches and compared various evaluation metrics. Our experiments led us to question the relevance of the current evaluation methods for the text revision task.

Écrire un article scientifique est une tâche difficile. L’écriture scientifique étant un genre très codifié, de bonnes compétences d’écriture sont essentielles pour transmettre ses idées et les résultats de ses recherches. Cet article décrit les motivations et les travaux préliminaires de la création du corpus CASIMIR dont l’objectif est d’offrir une ressource sur l’étape de révision du processus d’écriture d’un article scientifique. CASIMIR est un corpus des multiples versions de 26 355 articles scientifiques provenant d’OpenReview accompagné des relectures par les pairs.