Update README.md

README.md

@@ -33,7 +33,7 @@ Each instance has the following key-value pairs:
 
 ## Dataset Sources
 * **Respository**: https://github.com/hitz-zentroa/summarization
-* **Paper**: [Evaluating the Evaluator: Summarization Metrics and LLM-Judges beyond English](https://arxiv.org/abs/2503.17039)
+* **Paper**: [Evaluating the Evaluator: Summarization Metrics and LLM-Judges beyond English](http://journal.sepln.org/sepln/ojs/ojs/index.php/pln/article/view/6886)
 
 ## Acknowledgements
 
@@ -55,17 +55,20 @@ We release BASSE under a [CC BY-NC-SA 4.0 license](https://creativecommons.org/l
 **BibTeX:**
 
 ```
-@misc{barnes2025summarizationmetricsspanishbasque,
-      title={Evaluating the Evaluator: Summarization Metrics and LLM-Judges beyond English}, 
-      author={Jeremy Barnes and Naiara Perez and Alba Bonet-Jover and Begoña Altuna},
-      year={2025},
-      eprint={2503.17039},
-      archivePrefix={arXiv},
-      primaryClass={cs.CL},
-      url={https://arxiv.org/abs/2503.17039}, 
+@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-Jover, A., & Altuna, B. (2025). Evaluating the Evaluator: Summarization Metrics and LLM-Judges beyond English. _arXiv preprint arXiv:2503.17039_.
+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