Can LLMs Generate Coherent Summaries? Leveraging LLM Summarization for Spanish-Language News Articles

Automatic summarization is essential for processing the vast quantity of news articles. However, existing methods struggle with factual consistency, hallucinations, and English-centric evaluations. This paper investigates whether Large Language Models can generate coherent and factually grounded summaries of Spanish-language news articles, using the DACSA dataset as a benchmark. Several strategies are evaluated, including zero-shot prompting, one-shot prompting, fine-tuning of seq2seq models mBART and mT5, and a novel bottleneck prompting method that integrates attention-based salience scoring with Named Entity Recognition. Our results show that modern instruction-tuned language models can achieve competitive performance in zero- and one-shot settings, often approaching the performance of fine-tuned baselines. Our proposed bottleneck method enhances factual accuracy and content selection, leading to measurable improvements in ROUGE and BERTScore, especially for larger models such as LLaMA-3.1-70B and Gemma-2-9B. These results suggest that structured prompting can complement conventional approaches, offering an effective and cost-efficient alternative to full supervision. The results indicate that LLMs guided by entity-anchored bottlenecks provide a promising approach to multilingual summarization in domains with limited resources.