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    <abstract>Although context windows have expanded significantly in recent years, hallucinations in long-context summarization remain a challenge. Long novels are better suited than news or papers for researching these hallucinations, due to their intrinsic information and detailed descriptions of events and dialogues. However, current research lacks a multi-scale benchmark for hallucination detection in long-context novel summarization and does not fully explore how hallucinations change as the context grows longer. In this study, we propose LongNovel, a multi-scale long-context bilingual (Chinese and English) novel benchmark for hallucination detection. This benchmark is constructed from 29 Chinese novels (ranging from 16k to 100k tokens) and chapter-level data from the BookSum dataset. We design 8 hallucination types and employ a combination of Multi-Model Arbitration and Entity-Referenced Hallucination Generation to ensure both data authenticity and a balanced distribution of hallucination categories. Furthermore, we manually revise the content in the test set to guarantee data reliability. Extensive experimental results demonstrate that LongNovel is a challenging benchmark. We release LongNovel for future research. https://github.com/BDML-lab/LongNove</abstract>
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%0 Conference Proceedings
%T LongNovel: A Multi-Scale Benchmark for Hallucination Detection in Long-Context Novel Summarization
%A Zhang, Ruizhi
%A Chen, Jinwei
%A Xiangju, Lu
%A Yan, He
%A Yu, Mo
%A Zhu, Junmin
%A Zhang, Wei
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F zhang-etal-2026-longnovel
%X Although context windows have expanded significantly in recent years, hallucinations in long-context summarization remain a challenge. Long novels are better suited than news or papers for researching these hallucinations, due to their intrinsic information and detailed descriptions of events and dialogues. However, current research lacks a multi-scale benchmark for hallucination detection in long-context novel summarization and does not fully explore how hallucinations change as the context grows longer. In this study, we propose LongNovel, a multi-scale long-context bilingual (Chinese and English) novel benchmark for hallucination detection. This benchmark is constructed from 29 Chinese novels (ranging from 16k to 100k tokens) and chapter-level data from the BookSum dataset. We design 8 hallucination types and employ a combination of Multi-Model Arbitration and Entity-Referenced Hallucination Generation to ensure both data authenticity and a balanced distribution of hallucination categories. Furthermore, we manually revise the content in the test set to guarantee data reliability. Extensive experimental results demonstrate that LongNovel is a challenging benchmark. We release LongNovel for future research. https://github.com/BDML-lab/LongNove
%U https://aclanthology.org/2026.emnlp-main.1423/
%P 32478-32538

Markdown (Informal)

[LongNovel: A Multi-Scale Benchmark for Hallucination Detection in Long-Context Novel Summarization](https://aclanthology.org/2026.emnlp-main.1423/) (Zhang et al., EMNLP 2026)

• LongNovel: A Multi-Scale Benchmark for Hallucination Detection in Long-Context Novel Summarization (Zhang et al., EMNLP 2026)

ACL

• Ruizhi Zhang, Jinwei Chen, Lu Xiangju, He Yan, Mo Yu, Junmin Zhu, and Wei Zhang. 2026. LongNovel: A Multi-Scale Benchmark for Hallucination Detection in Long-Context Novel Summarization. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 32478–32538, Budapest, Hungary. Association for Computational Linguistics.