@inproceedings{adak-etal-2025-reversum,
    title = "{REV}er{S}um: A Multi-staged Retrieval-Augmented Generation Method to Enhance {W}ikipedia Tail Biographies through Personal Narratives",
    author = "Adak, Sayantan  and
      Meher, Pauras Mangesh  and
      Das, Paramita  and
      Mukherjee, Animesh",
    editor = "Rambow, Owen  and
      Wanner, Leo  and
      Apidianaki, Marianna  and
      Al-Khalifa, Hend  and
      Eugenio, Barbara Di  and
      Schockaert, Steven  and
      Darwish, Kareem  and
      Agarwal, Apoorv",
    booktitle = "Proceedings of the 31st International Conference on Computational Linguistics: Industry Track",
    month = jan,
    year = "2025",
    address = "Abu Dhabi, UAE",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2025.coling-industry.61/",
    pages = "732--750",
    abstract = "Wikipedia is an invaluable resource for factual information about a wide range of entities. However, the quality of articles on less-known entities often lags behind that of the well-known ones. This study proposes a novel approach to enhancing Wikipedia`s B and C category biography articles by leveraging personal narratives such as autobiographies and biographies. By utilizing a multi-staged retrieval-augmented generation technique {--} REVerSum {--} we aim to enrich the informational content of these lesser-known articles. Our study reveals that personal narratives can significantly improve the quality of Wikipedia articles, providing a rich source of reliable information that has been underutilized in previous studies. Based on crowd-based evaluation, REVerSum generated content outperforms the best performing baseline by 17{\%} in terms of integrability to the original Wikipedia article and 28.5{\%} in terms of informativeness."
}