Update README.md

README.md

@@ -45,6 +45,12 @@ I år var Cognitive Science i Aarhus den uddannelse i Danmark, der havde det fje
 ---
 # mT5-base fine-tuned for News article Summarisation ✏️🧾
 
-[Google's T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) base fine-tuned on [DaNewsroom] (https://aclanthology.org/2020.lrec-1.831/) dataset for **summarisation** downstream task.
+[Google's T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) base fine-tuned on [DaNewsroom](https://aclanthology.org/2020.lrec-1.831/) dataset for **summarisation** downstream task.
 
+# Model summary
 This repository contains a model for Danish abstractive summarisation of news articles. The summariser is based on a language-specific mT5-base, where the vocabulary is condensed to include tokens used in Danish and English. The model is fine-tuned using an abstractive subset of the DaNewsroom dataset (Varab & Schluter, 2020), according to the binned density categories employed in Newsroom (Grusky et al., 2019).
+
+# References
+Grusky, M., Naaman, M., & Artzi, Y. (2018). Newsroom: A Dataset of 1.3 Million Summaries with Diverse Extractive Strategies. ArXiv:1804.11283 [Cs]. http://arxiv.org/abs/1804.11283
+Varab, D., & Schluter, N. (2020). DaNewsroom: A Large-scale Danish Summarisation Dataset. Proceedings of the 12th Language Resources and Evaluation Conference, 6731–6739. https://aclanthology.org/2020.lrec-1.831
+