对比多种方法在低资源语言摘要中的表现，发现微调模型效果最佳。
Comparing Approaches to Automatic Summarization in Less-Resourced Languages
- 比较了大中小模型零样本提示、微调及翻译流水线等多种方法。
- 多语言微调的mT5在多数指标上优于零样本大模型。
- 大模型评分可靠性在低资源语言中下降，适合研究者参考。
自动文本摘要在英语等高资源语言中已取得优异性能，但在低资源语言中研究较少。本文比较了从大中小型大模型的零样本提示到使用三种数据增强方法微调mT5及多语言迁移等多种摘要方法，并探索了将源语言翻译成英语、摘要后再翻译回原语言的流水线方法。采用五种不同评估指标，结果表明：同参数量级的大模型性能存在差异；多语言微调的mT5基线在多数指标上优于其他方法，包括零样本大模型；大模型作为评判者在低资源语言上的可靠性降低。
原文摘要 · Abstract (English)
Automatic text summarization has achieved high performance in high-resourced languages like English, but comparatively less attention has been given to summarization in less-resourced languages. This work compares a variety of different approaches to summarization from zero-shot prompting of LLMs large and small to fine-tuning smaller models like mT5 with and without three data augmentation approaches and multilingual transfer. We also explore an LLM translation pipeline approach, translating from the source language to English, summarizing and translating back. Evaluating with five different metrics, we find that there is variation across LLMs in their performance across similar parameter sizes, that our multilingual fine-tuned mT5 baseline outperforms most other approaches including zero-shot LLM performance for most metrics, and that LLM as judge may be less reliable on less-resourced languages.
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