Spanish PoliSUM-2025: Evaluating Stylistic and Editorial Fidelity in Abstractive Summarization of Political News

Resumen

Automatic summarization of political news requires preserving factual content, journalistic style, and editorial orientation. However, research in Spanish remains limited due to the lack of ideologically diverse resources and outlet-sensitive evaluations. We present Spanish PoliSUM-2025, a corpus of 94,832 Spanish political news articles from multiple outlets with different editorial viewpoints and their original summaries. Using this resource, we compare mBART with three instruction-tuned large language models. Results show that instruction-tuned models consistently outperform the encoder–decoder baseline and that model size is the main determinant of quality. Linguistic analysis indicates that LLMs preserve semantic content but diverge from the concise, information-dense journalistic style. Substantial outlet-level variability appears in ROUGE-L but not in BERTScore, while ideological orientation produces no detectable differences under these metrics.