Papers › Hie-BART: Document Summarization with Hierarchical BART
Hie-BART: Document Summarization with Hierarchical BART
Kazuki Akiyama, Akihiro Tamura, Takashi Ninomiya
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).
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Document Summarization | CNN / Daily Mail | Hie-BART | ROUGE-1 | 44.35 | #7 of 26 | Archive leaderboard | report |
| Document Summarization | CNN / Daily Mail | Hie-BART | ROUGE-2 | 21.37 | #7 of 26 | Archive leaderboard | report |
| Document Summarization | CNN / Daily Mail | Hie-BART | ROUGE-L | 41.05 | #7 of 26 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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