Update README.md

README.md

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 News Abstractive Summarization for Catalan (NASCA) is a Transformer encoder-decoder model, with the same hyper-parameters than BART, to perform summarization of Catalan news articles. It is pre-trained on a combination of several self-supervised tasks that help to increase the abstractivity of the generated summaries. Four pre-training tasks have been combined: sentence permutation, text infilling, Gap Sentence Generation, and Next Segment Generation. Catalan newspapers, the Catalan subset of the OSCAR corpus and Wikipedia articles in Catalan were used for pre-training the model (9.3GB of raw text -2.5 millions of documents-).
 
 NASCA is finetuned for the summarization task on 636.596 (document, summary) pairs from the Dataset for Automatic summarization of Catalan and Spanish newspaper Articles (DACSA).
+
+More details about the pretraining/finetuning datasets and the models soon:
+
+@unpublished{DACSA,
+  author = "Vicent Ahuir, Lluís-F. Hurtado , José Ángel González and Encarna Segarra",
+  title = "DACSA: a Dataset for Automatic summarization of Catalan and Spanish
+    newspaper Articles",
+  note = "Unsubmitted",
+}
+
+@unpublished{NAS,
+  author = "Vicent Ahuir, Lluís-F. Hurtado , José Ángel González and Encarna Segarra",
+  title = "NAS CA and NAS ES : Two monolingual pre-trained models for
+abstractive summarization in Catalan and Spanish",
+  note = "Submitted to the Special Issue on Current Approaches and Applications in Natural Language Processing (Applied Sciences)",
+}