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    <abstract>Aspect-based summarization aims to generate summaries that highlight specific aspects of a text, enabling more personalized and targeted summaries. However, its application to books remains unexplored due to the difficulty of constructing reference summaries for long text. To address this challenge, we propose BookAsSumQA, a QA-based evaluation framework for aspect-based book summarization. BookAsSumQA automatically generates aspect-specific QA pairs from a narrative knowledge graph to evaluate summary quality based on its question-answering performance. Our experiments using BookAsSumQA revealed that while LLM-based approaches showed higher accuracy on shorter texts, RAG-based methods become more effective as document length increases, making them more efficient and practical for aspect-based book summarization.</abstract>
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%0 Conference Proceedings
%T BookAsSumQA: An Evaluation Framework for Aspect-Based Book Summarization via Question Answering
%A Miyazato, Ryuhei
%A Wei, Ting-Ruen
%A Wu, Xuyang
%A Wu, Hsin-Tai
%A Harada, Kei
%Y T.y.s.s, Santosh
%Y Shimizu, Shuichiro
%Y Gong, Yifan
%S The 14th International Joint Conference on Natural Language Processing and The 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics
%D 2025
%8 December
%I Association for Computational Linguistics
%C Mumbai, India
%@ 979-8-89176-304-3
%F miyazato-etal-2025-bookassumqa
%X Aspect-based summarization aims to generate summaries that highlight specific aspects of a text, enabling more personalized and targeted summaries. However, its application to books remains unexplored due to the difficulty of constructing reference summaries for long text. To address this challenge, we propose BookAsSumQA, a QA-based evaluation framework for aspect-based book summarization. BookAsSumQA automatically generates aspect-specific QA pairs from a narrative knowledge graph to evaluate summary quality based on its question-answering performance. Our experiments using BookAsSumQA revealed that while LLM-based approaches showed higher accuracy on shorter texts, RAG-based methods become more effective as document length increases, making them more efficient and practical for aspect-based book summarization.
%U https://aclanthology.org/2025.ijcnlp-srw.11/
%P 123-133

Markdown (Informal)

[BookAsSumQA: An Evaluation Framework for Aspect-Based Book Summarization via Question Answering](https://aclanthology.org/2025.ijcnlp-srw.11/) (Miyazato et al., IJCNLP 2025)

• BookAsSumQA: An Evaluation Framework for Aspect-Based Book Summarization via Question Answering (Miyazato et al., IJCNLP 2025)

ACL

• Ryuhei Miyazato, Ting-Ruen Wei, Xuyang Wu, Hsin-Tai Wu, and Kei Harada. 2025. BookAsSumQA: An Evaluation Framework for Aspect-Based Book Summarization via Question Answering. In The 14th International Joint Conference on Natural Language Processing and The 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics, pages 123–133, Mumbai, India. Association for Computational Linguistics.