@inproceedings{singhania-etal-2025-recall,
    title = "Recall Them All: Long List Generation from Long Novels",
    author = "Singhania, Sneha  and
      Razniewski, Simon  and
      Weikum, Gerhard",
    editor = "Arachchige, Isuri Nanomi  and
      Frontini, Francesca  and
      Mitkov, Ruslan  and
      Rayson, Paul",
    booktitle = "Proceedings of the First on Natural Language Processing and Language Models for Digital Humanities",
    month = sep,
    year = "2025",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd., Shoumen, Bulgaria",
    url = "https://preview.aclanthology.org/corrections-2026-01/2025.lm4dh-1.13/",
    pages = "133--142",
    abstract = "Language models can generate lists of salient literary characters for specific relations but struggle with long, complete lists spanning entire novels. This paper studies the non-standard setting of extracting complete entity lists from full-length books, such as identifying all 50+ friends of Harry Potter across the 7-volume book series. We construct a benchmark dataset with meticulously compiled ground-truth, posing it as a challenge for the research community. We present a first-cut method to tackle this task, based on RAG with LLMs. Our method introduces the novel contribution of harnessing IR-style pseudo-relevance feedback for effective passage retrieval from literary texts. Experimental results show that our approach clearly outperforms both LLM-only and standard RAG baselines, achieving higher recall while maintaining acceptable precision."
}