@inproceedings{trippel-etal-2026-din,
    title = "{DIN} 19461: A National Standard for Derived Text Formats",
    author = {Trippel, Thorsten  and
      Barth, Florian  and
      Tello, Jose Calvo  and
      Du, Keli  and
      Gen{\^e}t, Philippe  and
      Kurzawe, Daniel  and
      Leinen, Peter  and
      Lendvai, Piroska  and
      Sch{\"o}ch, Christof  and
      Witt, Andreas  and
      Zimmermann, Arden},
    editor = {Barth, Florian  and
      Du, Keli  and
      Calvo Tello, Jos{\'e}  and
      Gen{\^e}t, Philippe  and
      Lendvai, Piroska  and
      Sch{\"o}ch, Christof  and
      Trippel, Thorsten},
    booktitle = "Proceedings of Leveraging Derived Text Formats to Unlock Copyrighted Collections for Open Science ({DTF}) @ {LREC} 2026",
    month = jun,
    year = "2026",
    address = "Palma, Mallorca (Spain)",
    publisher = "ELRA Language Resources Association (ELRA)",
    url = "https://preview.aclanthology.org/test-year-match/2026.dtf-1.9/",
    doi = "10.63317/3u92avo9gg59",
    pages = "67--75",
    abstract = "We present DIN{~}19461:2026-06 (E), a German draft national standard that defines categories, terminology, and process requirements for Derived Text Formats (DTFs) created from text documents in natural language. The standard specifies enrichment and information reduction operations, requirements for combining multiple DTFs, and documentation obligations for publication, archiving, and reuse. Its aim is to enable legally compliant sharing and analysis of texts{--}especially where copyright or data protection prevents distributing originals{--}while maintaining scientific utility and reproducibility through explicit process and parameter recording. We outline the scope, the key concepts, the four core reduction operations (retain, delete, replace, randomise), together with examples across token-, structure-, and vector-based DTFs, and implications for infrastructures (e.g., ISO 24622-based metadata). Finally, we discuss limitations, open questions (e.g., reconstruction risks with modern ML models), and next steps for adoption and maintenance."
}