mlsanityData to 14 Oct 2026

A BART-based approach with hierarchical strategy for Vietnamese abstractive multi-document summarization

arXiv 2606.19591 ↗First posted 17 Jun 2026cs.CL cs.AIcited by 0

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Abstract (arXiv)

In this technical report, we focus on solving the challenge of Vietnamese multi-document abstractive summarization, introduced in the International Workshop on Vietnamese Language and Speech Processing (VLSP) 2022. We choose to follow the popular hierarchical approach, i.e. condensing each document followed by aggregation and summarization. We propose a novel yet simple strategy to shorten documents that is driven by the golden summary, thus ensuring high correlation between stages of the hierarchical approach. Our method achieves a ROUGE2-F1 score of 0.2468 on the VLSP's public test set, and can produce fluent and concise summaries. Additionally, we utilize external sources for extra data, which greatly enhances the quantity of data for Vietnamese multi-document summarization. The additional data is made available for the community.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 8 of its 15 references that are in the map. First posted on arXiv 17 Jun 2026.

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References · 8

  1. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,599
  2. 2019BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehensioncited by 5,893
  3. 2019PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarizationcited by 1,102
  4. 2019Multi-News: a Large-Scale Multi-Document Summarization Dataset and Abstractive Hierarchical Modelcited by 431
  5. 2021LongT5: Efficient Text-To-Text Transformer for Long Sequencescited by 232
  6. 2020Leveraging Graph to Improve Abstractive Multi-Document Summarizationcited by 67
  7. 2022ViT5: Pretrained Text-to-Text Transformer for Vietnamese Language Generationcited by 37
  8. 2021BARTpho: Pre-trained Sequence-to-Sequence Models for Vietnamesecited by 34

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