CASIMIR: A Corpus of Scientific Articles enhanced with Multiple Author-Integrated Revisions
This provides a resource for researchers studying scientific writing and revision processes, but it is incremental as it builds on existing datasets and methods.
The authors tackled the lack of a comprehensive dataset for studying scientific article revisions by creating CASIMIR, which includes 15,646 articles with multiple versions, sentence-level alignments, and automated edit annotations, and they found that current evaluation metrics for text revision are inadequate.
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.