Hie-BART: Document Summarization with Hierarchical BART

Kazuki Akiyama, Akihiro Tamura, Takashi Ninomiya
2021 Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Student Research Workshop   unpublished
This paper proposes a new abstractive summarization model for documents, hierarchical BART (Hie-BART), which captures the hierarchical structures of documents (i.e., their sentence-word structures) in the BART model. Although the existing BART model has achieved state-of-the-art performance on document summarization tasks, it does not account for interactions between sentence-level and word-level information. In machine translation tasks, the performance of neural machine translation models can
more » ... be improved with the incorporation of multi-granularity self-attention (MG-SA), which captures relationships between words and phrases. Inspired by previous work, the proposed Hie-BART model incorporates MG-SA into the encoder of the BART model for capturing sentence-word structures. Evaluations performed on the CNN/Daily Mail dataset show that the proposed Hie-BART model outperforms strong baselines and improves the performance of a non-hierarchical BART model (+0.23 ROUGE-L).
doi:10.18653/v1/2021.naacl-srw.20 fatcat:gdc7mw4tyzhwrjev2ydaudjdfi