@inproceedings{akiyama-etal-2021-hie,
    title = "Hie-{BART}: Document Summarization with Hierarchical {BART}",
    author = "Akiyama, Kazuki  and
      Tamura, Akihiro  and
      Ninomiya, Takashi",
    editor = "Durmus, Esin  and
      Gupta, Vivek  and
      Liu, Nelson  and
      Peng, Nanyun  and
      Su, Yu",
    booktitle = "Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Student Research Workshop",
    month = jun,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2021.naacl-srw.20/",
    doi = "10.18653/v1/2021.naacl-srw.20",
    pages = "159--165",
    abstract = "This paper proposes a new abstractive document summarization model, hierarchical BART (Hie-BART), which captures hierarchical structures of a document (i.e., sentence-word structures) in the BART model. Although the existing BART model has achieved a state-of-the-art performance on document summarization tasks, the model does not have the interactions between sentence-level information and word-level information. In machine translation tasks, the performance of neural machine translation models has been improved by incorporating multi-granularity self-attention (MG-SA), which captures the relationships between words and phrases. Inspired by the previous work, the proposed Hie-BART model incorporates MG-SA into the encoder of the BART model for capturing sentence-word structures. Evaluations on the CNN/Daily Mail dataset show that the proposed Hie-BART model outperforms some strong baselines and improves the performance of a non-hierarchical BART model (+0.23 ROUGE-L)."
}