Research Paper#Natural Language Processing, Summarization, Low-Resource Languages, LLMs🔬 ResearchAnalyzed: Jan 3, 2026 09:30
Summarization Approaches for Low-Resource Languages Compared
Analysis
This paper addresses a critical gap in NLP research by focusing on automatic summarization in less-resourced languages. It's important because it highlights the limitations of current summarization techniques when applied to languages with limited training data and explores various methods to improve performance in these scenarios. The comparison of different approaches, including LLMs, fine-tuning, and translation pipelines, provides valuable insights for researchers and practitioners working on low-resource language tasks. The evaluation of LLM as judge reliability is also a key contribution.
Key Takeaways
- •mT5 fine-tuning with multilingual data performs well for summarization in low-resource languages.
- •Zero-shot LLM performance varies across different LLMs.
- •LLMs as judges may be unreliable for evaluating summaries in low-resource languages.
Reference
“The multilingual fine-tuned mT5 baseline outperforms most other approaches including zero-shot LLM performance for most metrics.”