用分层策略提升越南语多文档摘要质量，基于BART模型实现高相关性摘要。
A BART-based approach with hierarchical strategy for Vietnamese abstractive multi-document summarization
- 采用分层结构：先压缩每篇文档，再聚合生成摘要。
- 在VLSP测试集上达到ROUGE2-F1 0.2468，生成流畅简洁的摘要。
- 引入外部数据增强训练，成果开源供社区使用。
本文针对国际越南语语言与语音处理研讨会（VLSP 2022）提出的越南语多文档抽象摘要挑战，采用流行的分层方法，即先对每篇文档进行压缩，再进行聚合与总结。提出一种基于黄金摘要驱动的新颖而简单的文档缩短策略，确保分层流程各阶段间的高度相关性。该方法在VLSP公开测试集上取得ROUGE2-F1为0.2468的成绩，可生成流畅且简洁的摘要。此外，通过利用外部数据源扩充训练数据，显著提升了越南语多文档摘要的数据量，新增数据已向社区开放。
原文摘要 · Abstract (English)
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.
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