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Datasets:
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BasqueSumm

Tasks:
Summarization
Text Generation
Fill-Mask
Modalities:
Text
Formats:
json
Languages:
Basque
Size:
10K - 100K
Libraries:
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pandas
Polars
License:
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metadata 
license: cc-by-nc-sa-4.0
task_categories:
  - summarization
  - text-generation
  - fill-mask
language:
  - eu
pretty_name: BasqueSumm
size_categories:
  - 100K<n<1M

BasqueSumm

BasqueSumm was automatically compiled from www.berria.eus using trafilatura to extract the texts.

Each instance has the following key-value pairs:

  • "date" (str): When the article was published, formatted as "yyyy-mm-dd".
  • "url" (str): The URL of the original publication.
  • "category" (str): the articles topic, e.g., economy, society.
  • "title" (str): The title of the article.
  • "subtitle" (str): The subtitle of the article.
  • "summary" (str): The combined title + subtitle, which acts as a proxy for a reference summary.
  • "text" (str): The news article.

Dataset Details

  • Curated by: Jeremy Barnes
  • Language(s) (NLP): Basque (es-EU)
  • License: CC BY-NC-SA 4.0

Dataset Sources

Acknowledgements

This work has been partially supported by the Basque Government (IKER-GAITU project), the Spanish Ministry for Digital Transformation and of Civil Service, and the EU-funded NextGenerationEU Recovery, Transformation and Resilience Plan (ILENIA project, 2022/TL-22/00215335 and 2022/TL22/00215334). Additional support was provided through DeepR3 (TED2021-130295B-C31) funded by MCIN/AEI/10.13039/501100011033 and European Union NextGeneration EU/PRTR; also through NL4DISMIS: Natural Language Technologies for dealing with dis- and misinformation (CIPROM/2021/021) and the grant CIBEST/2023/8, both funded by the Generalitat Valenciana.

Licensing

We release BASSE under a CC BY-NC-SA 4.0 license

Citation

BibTeX:

@article{barnes-etal-2025-basse,,
    author = {Jeremy Barnes y Naiara Perez y Alba Bonet y Begoña Altuna},
    title = {Evaluating the Evaluator: Summarization Metrics and LLM-Judges beyond English},
    journal = {Procesamiento del Lenguaje Natural},
    volume = {77},
    number = {0},
    year = {2026},
    abstract = {Automatic text summarization relies on automatic evaluation to quickly determine the quality of summarization models via automatic metrics and LLM-as-a-Judge models. However, these techniques require meta-evaluation to ensure that they capture human judgments correctly. In this paper, we explore this meta-evaluation beyond English by generating a new multilingual summary meta-evaluation dataset (BASSE), which comprises human judgments on 2,040 abstractive summaries, generated either manually or by five Large Language Models (LLMs) with four different prompts. For each summary, annotators evaluate five criteria on a 5-point Likert scale: coherence, consistency, fluency, relevance, and 5W1H. We then benchmark automatic summarization metrics and LLM-as-a-Judge models. Our results show that currently proprietary judge LLMs have the highest correlation with human judgments, followed by criteria-specific automatic metrics, while open-sourced judge LLMs perform poorly.},
    issn = {1989-7553},
    url = {http://journal.sepln.org/sepln/ojs/ojs/index.php/pln/article/view/6886},
    pages = {211--234}
}

APA:

Barnes, J., Perez, N., Bonet, A., & Altuna, B. (2026). Evaluating the Evaluator: Summarization Metrics and LLM-Judges beyond English. Procesamiento Del Lenguaje Natural, 77, 211-234. URL: http://journal.sepln.org/sepln/ojs/ojs/index.php/pln/article/view/6886