Multilingual Generation in Abstractive Summarization: A Comparative Study

Abstract

The emergence of pre-trained models marks a significant juncture for the multilingual generation, offering unprecedented capabilities to comprehend and produce text across multiple languages. These models display commendable efficiency in high-resource languages. However, their performance notably falters in low-resource languages due to the extensive linguistic diversity encountered. Moreover, the existing works lack thorough analysis impairs the discovery of effective multilingual strategies, further complicating the advancement of current multilingual generation systems. This paper aims to appraise the efficacy of multilingual generation tasks, with a focus on summarization, through three resource availability scenarios: high-resource, low-resource, and zero-shot. We classify multilingual generation methodologies into three foundational categories based on their underlying modeling principles: Fine-tuning, Parameter-isolation, and Constraint-based approaches. Following this classification, we conduct a comprehensive comparative study of these methodologies across different resource contexts using two datasets that span six languages. This analysis provides insights into the unique advantages and limitations of each method. In addition, we introduce an innovative yet simple automatic metric LANGM designed to mitigate the prevalent problem of spurious correlations associated with language mixing. LANGM accurately measures the degree of code-mixing at the language level. Finally, we highlight several challenges and suggest potential avenues for future inquiry, aiming to spur further advancements within the field of multilingual text generation.