updated the script

Controlled-Text-Reduction-dataset.py

@@ -251,7 +251,7 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-"""A Dataset loading script for the QA-Discourse dataset (Pyatkin et. al., ACL 2020)."""
+"""A Dataset loading script for the Controlled Text Reduction dataset."""
 
 
 import datasets
@@ -260,26 +260,43 @@ from typing import List
 import pandas as pd
 
 
-_CITATION = """\
-@inproceedings{pyatkin2020qadiscourse,
-  title={QADiscourse-Discourse Relations as QA Pairs: Representation, Crowdsourcing and Baselines},
-  author={Pyatkin, Valentina and Klein, Ayal and Tsarfaty, Reut and Dagan, Ido},
-  booktitle={Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)},
-  pages={2804--2819},
-  year={2020}
-}"""
+_CITATION = """"""
+# _CITATION = """\
+# @inproceedings{roit2020controlled,
+#   title={Controlled Crowdsourcing for High-Quality QA-SRL Annotation},
+#   author={Roit, Paul and Klein, Ayal and Stepanov, Daniela and Mamou, Jonathan and Michael, Julian and Stanovsky, Gabriel and Zettlemoyer, Luke and Dagan, Ido},
+#   booktitle={Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics},
+#   pages={7008--7013},
+#   year={2020}
+# }
+# """
 
 
 _DESCRIPTION = """\
-The dataset contains question-answer pairs to model discourse relations. 
-While answers roughly correspond to spans of the sentence, these spans could have been freely adjusted by annotators to grammaticaly fit the question;
-Therefore, answers are given just as text and not as identified spans of the original sentence.    
-See the paper for details: QADiscourse - Discourse Relations as QA Pairs: Representation, Crowdsourcing and Baselines, Pyatkin et. al., 2020
+The dataset contains document-summary pairs with document spans (referred to as "highlights"), indicating the "pre-selected" spans that lead to the creation of the summary.
+The evaluation and test datasets were constructed via controlled crowdsourcing.
+The train datasets were automatically generated using the summary-source proposition-level alignment model SuperPAL (Ernst et al., 2021).
 """
 
-_HOMEPAGE = "https://github.com/ValentinaPy/QADiscourse"
-
-_LICENSE = """Resources on this page are licensed CC-BY 4.0, a Creative Commons license requiring Attribution (https://creativecommons.org/licenses/by/4.0/)."""
+_HOMEPAGE = "https://github.com/lovodkin93/Controlled_Text_Reduction/tree/main"
+
+_LICENSE = """MIT License
+Copyright (c) 2022 lovodkin93
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE."""
 
 
 _URLs = {