Update README

README.md

@@ -20,7 +20,7 @@ task_ids:
 paperswithcode_id: cnn-daily-mail-1
 pretty_name: CNN / Daily Mail
 ---
-# Dataset Card for CNN Dailymail Dataset
+# Dataset Card for CNN Dailymail Dutch 🇳🇱🇧🇪 Dataset
 
 ## Table of Contents
 - [Dataset Description](#dataset-description)
@@ -48,6 +48,8 @@ pretty_name: CNN / Daily Mail
 
 ## Dataset Description
 
+Note: the data below is from the English version at [cnn_dailymail](https://huggingface.co/datasets/cnn_dailymail).
+
 - **Homepage:**
 - **Repository:** [CNN / DailyMail Dataset repository](https://github.com/abisee/cnn-dailymail)
 - **Paper:** [Abstractive Text Summarization Using Sequence-to-Sequence RNNs and Beyond](https://papers.nips.cc/paper/5945-teaching-machines-to-read-and-comprehend.pdf), [Get To The Point: Summarization with Pointer-Generator Networks](https://www.aclweb.org/anthology/K16-1028.pdf)
@@ -56,11 +58,15 @@ pretty_name: CNN / Daily Mail
 
 ### Dataset Summary
 
-The CNN / DailyMail Dutch Dataset is an English-language dataset translated to Dutch containing just over 300k unique news articles as written by journalists at CNN and the Daily Mail. The current version supports both extractive and abstractive summarization, though the original version was created for machine reading and comprehension and abstractive question answering. 
+The CNN / DailyMail Dutch 🇳🇱🇧🇪 Dataset is an English-language dataset translated to Dutch containing just over 300k unique news articles as written by journalists at CNN and the Daily Mail. The current version supports both extractive and abstractive summarization, though the original version was created for machine reading and comprehension and abstractive question answering. 
+
+*This dataset currently (Aug '22) has a single config, which is
+config `3.0.0` of [cnn_dailymail](https://huggingface.co/datasets/cnn_dailymail) translated to Dutch
+with [yhavinga/t5-base-36L-ccmatrix-multi](https://huggingface.co/yhavinga/t5-base-36L-ccmatrix-multi).*
 
 ### Supported Tasks and Leaderboards
 
-- 'summarization': [Versions 2.0.0 and 3.0.0 of the CNN / DailyMail Dataset](https://www.aclweb.org/anthology/K16-1028.pdf) can be used to train a model for abstractive and extractive summarization ([Version 1.0.0](https://papers.nips.cc/paper/5945-teaching-machines-to-read-and-comprehend.pdf) was developed for machine reading and comprehension and abstractive question answering). The model performance is measured by how high the output summary's [ROUGE](https://huggingface.co/metrics/rouge) score for a given article is when compared to the highlight as written by the original article author. [Zhong et al (2020)](https://www.aclweb.org/anthology/2020.acl-main.552.pdf) report a ROUGE-1 score of 44.41 when testing a model trained for extractive summarization. See the [Papers With Code leaderboard](https://paperswithcode.com/sota/document-summarization-on-cnn-daily-mail) for more models. 
+- 'summarization': [Version 3.0.0 of the CNN / DailyMail Dataset](https://www.aclweb.org/anthology/K16-1028.pdf) can be used to train a model for abstractive and extractive summarization ([Version 1.0.0](https://papers.nips.cc/paper/5945-teaching-machines-to-read-and-comprehend.pdf) was developed for machine reading and comprehension and abstractive question answering). The model performance is measured by how high the output summary's [ROUGE](https://huggingface.co/metrics/rouge) score for a given article is when compared to the highlight as written by the original article author. [Zhong et al (2020)](https://www.aclweb.org/anthology/2020.acl-main.552.pdf) report a ROUGE-1 score of 44.41 when testing a model trained for extractive summarization. See the [Papers With Code leaderboard](https://paperswithcode.com/sota/document-summarization-on-cnn-daily-mail) for more models.
 
 ### Languages