🤖 AI Summary
This study addresses the challenges of information redundancy and coherence in Vietnamese multi-document abstractive summarization by proposing a hierarchical summarization framework. The approach first compresses individual documents using a reference-summary-guided strategy and then aggregates the compressed representations to generate the final summary, thereby enhancing content relevance at each stage and improving overall consistency. Built upon the BART architecture and augmented with external data, the system achieves a ROUGE-2 F1 score of 0.2468 on the VLSP 2022 test set. Additionally, the authors release an expanded Vietnamese multi-document summarization dataset to support further research on summarization for low-resource languages.
📝 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 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.