@inproceedings{jourdan-etal-2026-earlyscirev,
    title = "{E}arly{S}ci{R}ev: A Dataset of Early-Stage Scientific Revisions Extracted from {L}a{T}e{X} Writing Traces",
    author = "Jourdan, L{\'e}ane  and
      Aubert-B{\'e}duchaud, Julien  and
      Chupin, Yannis  and
      Baccari, Marah  and
      Boudin, Florian",
    editor = "Rehm, Georg  and
      Dietze, Stefan  and
      Dessi, Danilo  and
      Maynard, Diana  and
      Schimmler, Sonja",
    booktitle = "Proceedings of Natural Scientific Language Processing ({NSLP}) @ {LREC} 2026",
    month = may,
    year = "2026",
    address = "Palma, Mallorca (Spain)",
    publisher = "ELRA Language Resources Association (ELRA)",
    url = "https://preview.aclanthology.org/ingest-nlpsi/2026.nslp-1.12/",
    doi = "10.63317/4fpwcgmuqk8x",
    pages = "119--126",
    abstract = "Scientific writing is an iterative process that generates rich revision traces, yet publicly available resources typically expose only final or near-final versions of papers. This limits empirical study of revision behaviour and evaluation of large language models (LLMs) for scientific writing. We introduce EarlySciRev, a dataset of early-stage scientific text revisions automatically extracted from arXiv LaTeX source files. Our key observation is that commented-out text in LaTeX often preserves discarded or alternative formulations written by the authors themselves. By aligning commented segments with nearby final text, we extract paragraph-level candidate revision pairs and apply LLM-based filtering to retain genuine revisions. Starting from 1.28M candidate pairs, our pipeline yields 578k validated revision pairs, grounded in authentic early drafting traces. We additionally provide a human-annotated benchmark for revision detection. EarlySciRev complements existing resources focused on late-stage revisions or synthetic rewrites and supports research on scientific writing dynamics, revision modelling, and LLM-assisted editing."
}