@inproceedings{hu-etal-2024-improving,
    title = "Improving Factual Consistency in Abstractive Summarization with Sentence Structure Pruning",
    author = "Hu, Dingxin  and
      Zhang, Xuanyu  and
      Zhang, Xingyue  and
      Li, Yiyang  and
      Chen, Dongsheng  and
      Litvak, Marina  and
      Vanetik, Natalia  and
      Yang, Qing  and
      Xu, Dongliang  and
      Zhou, Yanquan  and
      Li, Lei  and
      Li, Yuze  and
      Zhu, Yingqi",
    editor = "Calzolari, Nicoletta  and
      Kan, Min-Yen  and
      Hoste, Veronique  and
      Lenci, Alessandro  and
      Sakti, Sakriani  and
      Xue, Nianwen",
    booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.lrec-main.770",
    pages = "8792--8803",
    abstract = "State-of-the-art abstractive summarization models still suffer from the content contradiction between the summaries and the input text, which is referred to as the factual inconsistency problem. Recently, a large number of works have also been proposed to evaluate factual consistency or improve it by post-editing methods. However, these post-editing methods typically focus on replacing suspicious entities, failing to identify and modify incorrect content hidden in sentence structures. In this paper, we first verify that the correctable errors can be enriched by leveraging sentence structure pruning operation, and then we propose a post-editing method based on that. In the correction process, the pruning operation on possible errors is performed on the syntactic dependency tree with the guidance of multiple factual evaluation metrics. Experimenting on the FRANK dataset shows a great improvement in factual consistency compared with strong baselines and, when combined with them, can achieve even better performance. All the codes and data will be released on paper acceptance.",
}

State-of-the-art abstractive summarization models still suffer from the content contradiction between the summaries and the input text, which is referred to as the factual inconsistency problem. Recently, a large number of works have also been proposed to evaluate factual consistency or improve it by post-editing methods. However, these post-editing methods typically focus on replacing suspicious entities, failing to identify and modify incorrect content hidden in sentence structures. In this paper, we first verify that the correctable errors can be enriched by leveraging sentence structure pruning operation, and then we propose a post-editing method based on that. In the correction process, the pruning operation on possible errors is performed on the syntactic dependency tree with the guidance of multiple factual evaluation metrics. Experimenting on the FRANK dataset shows a great improvement in factual consistency compared with strong baselines and, when combined with them, can achieve even better performance. All the codes and data will be released on paper acceptance.
Hu, Dingxin Zhang, Xuanyu Zhang, Xingyue Li, Yiyang Chen, Dongsheng Litvak, Marina Vanetik, Natalia Yang, Qing Xu, Dongliang Zhou, Yanquan Li, Lei Li, Yuze Zhu, Yingqi
Improving Factual Consistency in Abstractive Summarization with Sentence Structure Pruning