Monolingual versus Multilingual BERTology for Vietnamese Extractive Multi-Document Summarization

TL;DR

This paper explores the effectiveness of monolingual versus multilingual BERT models for extractive multi-document summarization in Vietnamese, showing monolingual models outperform multilingual ones.

Contribution

It introduces a comparative analysis of monolingual and multilingual BERT models specifically for Vietnamese multi-document summarization.

Findings

Monolingual BERT models outperform multilingual models in Vietnamese summarization.

The proposed approach achieves promising results compared to previous models.

Multilingual BERT models are less effective than monolingual models for Vietnamese NLP tasks.

Abstract

Recent researches have demonstrated that BERT shows potential in a wide range of natural language processing tasks. It is adopted as an encoder for many state-of-the-art automatic summarizing systems, which achieve excellent performance. However, so far, there is not much work done for Vietnamese. In this paper, we showcase how BERT can be implemented for extractive text summarization in Vietnamese on multi-document. We introduce a novel comparison between different multilingual and monolingual BERT models. The experiment results indicate that monolingual models produce promising results compared to other multilingual models and previous text summarizing models for Vietnamese.