@inproceedings{liu-etal-2025-sara,
    title = "{SARA}: Salience-Aware Reinforced Adaptive Decoding for Large Language Models in Abstractive Summarization",
    author = "Liu, Nayu  and
      Zhu, Junnan  and
      Ma, Yiming  and
      Lu, Zhicong  and
      Xu, Wenlei  and
      Yang, Yong  and
      Zhong, Jiang  and
      Wei, Kaiwen",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingestion-acl-25/2025.acl-long.1236/",
    pages = "25450--25463",
    ISBN = "979-8-89176-251-0",
    abstract = "LLMs have improved the fluency and informativeness of abstractive summarization but remain prone to hallucinations, where generated content deviates from the source document. Recent PMI decoding strategies mitigate over-reliance on prior knowledge by comparing output probabilities with and without source documents, effectively enhancing contextual utilization and improving faithfulness. However, existing strategies often neglect the explicit use of salient contextual information and rely on static hyperparameters to fix the balance between contextual and prior knowledge, limiting their flexibility. In this work, we propose Salience-Aware Reinforced Adaptive decoding (SARA), which incorporates salient information and allows the model to adaptively determine reliance on the source document{'}s context, salient context, and the model{'}s prior knowledge based on pointwise mutual information. Moreover, a tokenwise adaptive decoding mechanism via reinforcement learning is proposed in SARA to dynamically adjust the contributions of context and prior knowledge at each decoding timestep. Experiments on CNN/DM, WikiHow, and NYT50 datasets show that SARA consistently improves the quality and faithfulness of summaries across various LLM backbones without modifying their weights."
}