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

Researchers propose a BART-based hierarchical approach for Vietnamese multi-document abstractive summarization, achieving a ROUGE2-F1 score of 0.2468 on the VLSP 2022 benchmark. The method uses a novel document-shortening strategy guided by golden summaries and includes additional training data for the Vietnamese NLP community.

This technical report addresses a specialized challenge in natural language processing: abstractive multi-document summarization for Vietnamese, a lower-resourced language compared to English. The researchers employ a hierarchical pipeline that condensates individual documents before aggregating and generating final summaries, a proven architectural pattern for handling multiple sources. What distinguishes their approach is a straightforward yet effective shortening strategy that maintains consistency across pipeline stages by optimizing for correlation with reference summaries.

Vietnamese language processing remains underrepresented in AI research despite the language's 95+ million speakers. The VLSP workshop serves as a crucial venue for advancing Vietnamese NLP capabilities. This work follows growing industry recognition that non-English language models require dedicated research investments rather than assuming English-trained models generalize effectively. The authors enhance their contribution by releasing additional training data, addressing a critical bottleneck for Vietnamese NLP development.

The reported ROUGE2-F1 score of 0.2468 provides a quantitative baseline for the community, enabling future researchers to benchmark improvements. Higher performance on summarization tasks directly improves information processing efficiency for Vietnamese-speaking users and enterprises relying on automated document analysis. This extends practical applications across news aggregation, legal document review, and research paper synthesis.

Looking forward, the released dataset becomes a shared resource accelerating Vietnamese NLP progress. Success in multi-document summarization for Vietnamese could inspire similar efforts for other Asian languages facing similar resource constraints, gradually expanding quality AI capabilities across linguistic communities.

• →Researchers develop BART-based hierarchical approach achieving 0.2468 ROUGE2-F1 on Vietnamese multi-document summarization
• →Novel strategy shortens documents using golden summary guidance to maintain inter-stage consistency
• →Additional training data released to Vietnamese NLP community addresses critical resource shortage
• →Method demonstrates that lower-resourced languages benefit from specialized architectural designs
• →Benchmark enables future improvements in Vietnamese information processing and document automation