Update README.md

README.md

@@ -94,38 +94,27 @@ For each instance, there is a string for the article, a string for the highlight
 - `highlights`: a string containing the highlight of the article as written by the article author
 
 
-## Dataset Creation
+### Data Source
 
-### Curation Rationale
-
-Version 1.0.0 aimed to support supervised neural methodologies for machine reading and question answering with a large amount of real natural language training data and released about 313k unique articles and nearly 1M Cloze style questions to go with the articles. Versions 2.0.0 and 3.0.0 changed the structure of the dataset to support summarization rather than question answering. Version 3.0.0 provided a non-anonymized version of the data, whereas both the previous versions were preprocessed to replace named entities with unique identifier labels. 
-
-### Source Data
+This Multi-Eup dataset was collected from European Parliament. <https://www.europarl.europa.eu/portal/en>
 
 #### Initial Data Collection and Normalization
 
-The code for the non-anonymized version is made publicly available by Abigail See of Stanford University, Peter J. Liu of Google Brain and Christopher D. Manning of Stanford University at <https://github.com/abisee/cnn-dailymail>. The work at Stanford University was supported by the DARPA DEFT ProgramAFRL contract no. FA8750-13-2-0040.
+The code for the EMNLP MRL version is made publicly available by Jinrui Yang, Timothy Baldwin and Trevor Cohn of The University of Melbourne at <https://github.com/abisee/cnn-dailymail>. This research was funded by Melbourne Research Scholarship and undertaken using the LIEF HPCGPGPU Facility hosted at the University of Melbourne. This facility was established with the assistance of LIEF Grant LE170100200. 
 
-### Ethic
+### Ethics Statement
 
-The CNN / Daily Mail dataset version 1.0.0 is released under the [Apache-2.0 License](http://www.apache.org/licenses/LICENSE-2.0). 
+The dataset contains publicly-available EP data that does not include personal or sensitive information, with the exception of information relating to public officeholders, e.g., the names of the active members of the European Parliament, European Council, or other official administration bodies. The collected data is licensed under the Creative Commons Attribution 4.0 International licence. <https://eur-lex.europa.eu/content/legal-notice/legal-notice.html>
 
 ### Citation Information
 
 ```
-@inproceedings{see-etal-2017-get,
-    title = "Get To The Point: Summarization with Pointer-Generator Networks",
-    author = "See, Abigail  and
-      Liu, Peter J.  and
-      Manning, Christopher D.",
-    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
-    month = jul,
-    year = "2017",
-    address = "Vancouver, Canada",
-    publisher = "Association for Computational Linguistics",
-    url = "https://www.aclweb.org/anthology/P17-1099",
-    doi = "10.18653/v1/P17-1099",
-    pages = "1073--1083",
-    abstract = "Neural sequence-to-sequence models have provided a viable new approach for abstractive text summarization (meaning they are not restricted to simply selecting and rearranging passages from the original text). However, these models have two shortcomings: they are liable to reproduce factual details inaccurately, and they tend to repeat themselves. In this work we propose a novel architecture that augments the standard sequence-to-sequence attentional model in two orthogonal ways. First, we use a hybrid pointer-generator network that can copy words from the source text via pointing, which aids accurate reproduction of information, while retaining the ability to produce novel words through the generator. Second, we use coverage to keep track of what has been summarized, which discourages repetition. We apply our model to the CNN / Daily Mail summarization task, outperforming the current abstractive state-of-the-art by at least 2 ROUGE points.",
+@misc{yang2023multieup,
+      title={Multi-EuP: The Multilingual European Parliament Dataset for Analysis of Bias in Information Retrieval}, 
+      author={Jinrui Yang and Timothy Baldwin and Trevor Cohn},
+      year={2023},
+      eprint={2311.01870},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL}
 }
 ```