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        <title>BookWorm: A Dataset for Character Description and Analysis</title>
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        <namePart type="given">Argyrios</namePart>
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    <name type="personal">
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            <namePart type="given">Mohit</namePart>
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    <abstract>Characters are at the heart of every story, driving the plot and engaging readers. In this study, we explore the understanding of characters in full-length books, which contain complex narratives and numerous interacting characters. We define two tasks: character description, which generates a brief factual profile, and character analysis, which offers an in-depth interpretation, including character development, personality, and social context. We introduce the BookWorm dataset, pairing books from the Gutenberg Project with human-written descriptions and analyses. Using this dataset, we evaluate state-of-the-art long-context models in zero-shot and fine-tuning settings, utilizing both retrieval-based and hierarchical processing for book-length inputs. Our findings show that retrieval-based approaches outperform hierarchical ones in both tasks. Additionally, fine-tuned models using coreference-based retrieval produce the most factual descriptions, as measured by fact- and entailment-based metrics. We hope our dataset, experiments, and analysis will inspire further research in character-based narrative understanding.</abstract>
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    <identifier type="doi">10.18653/v1/2024.findings-emnlp.258</identifier>
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        <date>2024-11</date>
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%0 Conference Proceedings
%T BookWorm: A Dataset for Character Description and Analysis
%A Papoudakis, Argyrios
%A Lapata, Mirella
%A Keller, Frank
%Y Al-Onaizan, Yaser
%Y Bansal, Mohit
%Y Chen, Yun-Nung
%S Findings of the Association for Computational Linguistics: EMNLP 2024
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F papoudakis-etal-2024-bookworm
%X Characters are at the heart of every story, driving the plot and engaging readers. In this study, we explore the understanding of characters in full-length books, which contain complex narratives and numerous interacting characters. We define two tasks: character description, which generates a brief factual profile, and character analysis, which offers an in-depth interpretation, including character development, personality, and social context. We introduce the BookWorm dataset, pairing books from the Gutenberg Project with human-written descriptions and analyses. Using this dataset, we evaluate state-of-the-art long-context models in zero-shot and fine-tuning settings, utilizing both retrieval-based and hierarchical processing for book-length inputs. Our findings show that retrieval-based approaches outperform hierarchical ones in both tasks. Additionally, fine-tuned models using coreference-based retrieval produce the most factual descriptions, as measured by fact- and entailment-based metrics. We hope our dataset, experiments, and analysis will inspire further research in character-based narrative understanding.
%R 10.18653/v1/2024.findings-emnlp.258
%U https://aclanthology.org/2024.findings-emnlp.258/
%U https://doi.org/10.18653/v1/2024.findings-emnlp.258
%P 4471-4500

Markdown (Informal)

[BookWorm: A Dataset for Character Description and Analysis](https://aclanthology.org/2024.findings-emnlp.258/) (Papoudakis et al., Findings 2024)

• BookWorm: A Dataset for Character Description and Analysis (Papoudakis et al., Findings 2024)

ACL

• Argyrios Papoudakis, Mirella Lapata, and Frank Keller. 2024. BookWorm: A Dataset for Character Description and Analysis. In Findings of the Association for Computational Linguistics: EMNLP 2024, pages 4471–4500, Miami, Florida, USA. Association for Computational Linguistics.