A BART-based approach with hierarchical strategy for Vietnamese abstractive multi-document summarization
arXiv:2606.19591v1 Announce Type: new Abstract: 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 sco
The paper leverages recent advancements in large language models like BART to address a specific challenge in Vietnamese language processing, indicating active research and development in localized AI applications.
This work demonstrates progress in applying advanced AI techniques to less-resourced languages, which is crucial for digital inclusion and the development of language-specific AI capabilities, potentially leading to more diverse and equitable AI development.
The development of a robust abstractive multi-document summarization method for Vietnamese improves accessibility to complex information in that language and contributes to the broader field of natural language processing for low-resource languages.
- · Vietnamese language AI developers
- · NLP researchers
- · Information summarization platforms
- · Monolingual AI solutions
Improved automated summarization tools become available for Vietnamese documents.
Increased efficiency in information processing for businesses and government agencies operating in Vietnam.
Reduced information asymmetry and enhanced knowledge sharing within Vietnamese-speaking communities globally.
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Read at arXiv cs.CL