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An intrinsic hallucination optimization method for generative text summarization models
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Neng LI, Chengcheng YU, Qun LIU
Journal of Chongqing University of Posts and Telecommunications(Natural Science Edition) | 2025, 37(5) : 688 - 695
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Journal of Chongqing University of Posts and Telecommunications(Natural Science Edition) | 2025, 37(5): 688-695
• Artificial Intelligenceand Big Data •
An intrinsic hallucination optimization method for generative text summarization models
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Neng LI, Chengcheng YU, Qun LIU
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  • Chongqing Key Laboratory of Computational Intelligence, Chongqing University of Posts and Telecommunications, Chongqing 400065, P R China
doi: 10.3979/j.issn.1673-825X.202409020228
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Generative text summarization models can produce novel expressions in summaries, but even the most advanced models may generate content that contradicts the source text or lacks factual verifiability—a phenomenon known as hallucination. To address this issue, this paper proposes an intrinsic hallucination optimization method to improve the summarization generation process. The proposed approach mitigates hallucinations from three perspectives: data-level optimization, model training-level optimization, and summary generation strategy-level optimization. Experiments conducted on two benchmark datasets demonstrate the superior performance of the proposed method. Compared with baseline models, the proposed approach achieves an average improvement of 8.58% in R-1 score on the CNNDM dataset and 7.26% on the XSUM dataset. The results indicate that the method not only enhances summary quality but also effectively reduces hallucinations, providing a valuable reference for the practical deployment of generative text summarization models.

generative text summarization  /  intrinsic hallucination  /  candidate summaries  /  large language model
Neng LI, Chengcheng YU, Qun LIU. An intrinsic hallucination optimization method for generative text summarization models[J]. Journal of Chongqing University of Posts and Telecommunications(Natural Science Edition), 2025 , 37 (5) : 688 -695 . DOI: 10.3979/j.issn.1673-825X.202409020228
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Year 2025 volume 37 Issue 5
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doi: 10.3979/j.issn.1673-825X.202409020228
  • Receive Date：2024-09-02
  • Online Date：2026-04-16
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  • Received：2024-09-02
  • Revised：2025-03-05
Affiliations
    Chongqing Key Laboratory of Computational Intelligence, Chongqing University of Posts and Telecommunications, Chongqing 400065, P R China
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https://castjournals.cast.org.cn/joweb/cqyddxxb/EN/10.3979/j.issn.1673-825X.202409020228
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