@inproceedings{tan-etal-2024-turn,
    title = "How to Turn Card Catalogs into {LLM} Fodder",
    author = "Tan, Mary Ann  and
      Jiang, Shufan  and
      Sack, Harald",
    editor = "S{\'e}rasset, Gilles  and
      Oliveira, Hugo Gon{\c{c}}alo  and
      Oleskeviciene, Giedre Valunaite",
    booktitle = "Proceedings of the Workshop on Deep Learning and Linked Data (DLnLD) @ LREC-COLING 2024",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.dlnld-1.5",
    pages = "53--65",
    abstract = "Bibliographical metadata collections describing pre-modern objects suffer from incompleteness and inaccuracies. This hampers the identification of literary works. In addition, titles often contain voluminous descriptive texts that do not adhere to contemporary title conventions. This paper explores several NLP approaches where greater textual length in titles is leveraged to enhance descriptive information.",
}

Bibliographical metadata collections describing pre-modern objects suffer from incompleteness and inaccuracies. This hampers the identification of literary works. In addition, titles often contain voluminous descriptive texts that do not adhere to contemporary title conventions. This paper explores several NLP approaches where greater textual length in titles is leveraged to enhance descriptive information.
Tan, Mary Ann Jiang, Shufan Sack, Harald
How to Turn Card Catalogs into LLM Fodder