Datasets:

biu-nlp
/

Controlled-Text-Reduction-dataset

Modalities:

Text

Size:

100K - 1M

ArXiv:

Libraries:

Datasets

Dataset card Viewer Files Files and versions Community

Controlled-Text-Reduction-dataset

File size: 20,442 Bytes

# # coding=utf-8
# # Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
# #
# # Licensed under the Apache License, Version 2.0 (the "License");
# # you may not use this file except in compliance with the License.
# # You may obtain a copy of the License at
# #
# #     http://www.apache.org/licenses/LICENSE-2.0
# #
# # Unless required by applicable law or agreed to in writing, software
# # distributed under the License is distributed on an "AS IS" BASIS,
# # 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 Controlled Text Reduction dataset."""


# import datasets
# from dataclasses import dataclass
# from pathlib import Path
# from typing import List, Tuple
# import pandas as pd
# import json
# import gzip
# import itertools


# _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 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/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 = {
# #     "csv": {
# #         "sentences": {
# #             "wikinews.dev": "https://github.com/plroit/qasrl-gs/raw/master/data/sentences/wikinews.dev.full.csv",
# #             "wikinews.test": "https://github.com/plroit/qasrl-gs/raw/master/data/sentences/wikinews.test.full.csv",
# #             "wikipedia.dev": "https://github.com/plroit/qasrl-gs/raw/master/data/sentences/wikipedia.dev.full.csv",
# #             "wikipedia.test": "https://github.com/plroit/qasrl-gs/raw/master/data/sentences/wikipedia.test.full.csv",
# #         },
# #         "qasrl-annotations": {
# #             "wikinews.dev": "https://github.com/plroit/qasrl-gs/raw/master/data/gold/wikinews.dev.gold.csv",
# #             "wikinews.test": "https://github.com/plroit/qasrl-gs/raw/master/data/gold/wikinews.test.gold.csv",
# #             "wikipedia.dev": "https://github.com/plroit/qasrl-gs/raw/master/data/gold/wikipedia.dev.gold.csv",
# #             "wikipedia.test": "https://github.com/plroit/qasrl-gs/raw/master/data/gold/wikipedia.test.gold.csv",
# #         }, 
# #     },
# #     "jsonl": "https://qasrl.org/data/qasrl-gs.tar"       
# # }

# _URLs = {
#     "DUC-2001-2002": {
#         "dev": "https://github.com/lovodkin93/Controlled_Text_Reduction/tree/main/data/dev_DUC-2001-2002.csv",
#         "test": "https://github.com/lovodkin93/Controlled_Text_Reduction/tree/main/data/test_DUC-2001-2002.csv",
#         "train": "https://github.com/lovodkin93/Controlled_Text_Reduction/tree/main/data/train_DUC-2001-2002.csv"
#     },  
#     "CNN-DM": {
#         "train": "https://github.com/lovodkin93/Controlled_Text_Reduction/tree/main/data/train_CNNDM.csv",
#         "dev": "https://github.com/lovodkin93/Controlled_Text_Reduction/tree/main/data/dev_DUC-2001-2002.csv",
#         "test": "https://github.com/lovodkin93/Controlled_Text_Reduction/tree/main/data/test_DUC-2001-2002.csv",
#     },    
# }


# @dataclass
# class ControlledTextReductionConfig(datasets.BuilderConfig):
#     """ Allow the loader to re-distribute the original dev and test splits between train, dev and test. """
#     data_source: str = "DUC-2001-2002" # "DUC-2001-2002" or "CNN-DM"



# class ControlledTextReduction(datasets.GeneratorBasedBuilder):
#     """Controlled Text Reduction: dataset for the Controlled Text Reduction task ().
#     Each data point consists of a document, a summary, and a list of spans of the document that are the pre-selected content whose summary is the summary"""


#     VERSION = datasets.Version("1.0.0")

#     BUILDER_CONFIG_CLASS = ControlledTextReductionConfig

#     BUILDER_CONFIGS = [
#         ControlledTextReductionConfig(
#             name="DUC-2001-2002", 
#             version=VERSION, 
#             description="This provides the Controlled Text Reduction dataset extracted from the DUC 2001-2002 Single Document Summarization benchmark",
#             data_source="DUC-2001-2002"
#         ),
#         ControlledTextReductionConfig(
#             name="CNN-DM", 
#             version=VERSION, 
#             description="This provides the Controlled Text Reduction dataset extracted from the CNN-DM dataset (the train split)",
#             data_source="CNN-DM"
#         )
#     ]

#     DEFAULT_CONFIG_NAME = (
#         "DUC-2001-2002"  # It's not mandatory to have a default configuration. Just use one if it make sense.
#     )

#     def _info(self):
#         features = datasets.Features(
#             {
#                 "doc_text": datasets.Value("string"),
#                 "summary_text": datasets.Value("string"),
#                 "highlight_spans": datasets.Value("string")
#             }
#         )
#         return datasets.DatasetInfo(
#             # This is the description that will appear on the datasets page.
#             description=_DESCRIPTION,
#             # This defines the different columns of the dataset and their types
#             features=features,  # Here we define them above because they are different between the two configurations
#             # If there's a common (input, target) tuple from the features,
#             # specify them here. They'll be used if as_supervised=True in
#             # builder.as_dataset.
#             supervised_keys=None,
#             # Homepage of the dataset for documentation
#             homepage=_HOMEPAGE,
#             # License for the dataset if available
#             license=_LICENSE,
#             # Citation for the dataset
#             citation=_CITATION,
#         )
            
#     def _split_generators(self, dl_manager: datasets.utils.download_manager.DownloadManager):
#         """Returns SplitGenerators."""            
        
#         URLs = _URLs[self.config.data_source]
#         # Download and prepare all files - keep same structure as URLs 
#         corpora = {section:  Path(dl_manager.download_and_extract(URLs[section])) 
#                    for section in URLs} 
        
#         if self.config.data_source=="CNN-DM":
#             return [
#                 datasets.SplitGenerator(
#                     name=datasets.Split.TRAIN,
#                     # These kwargs will be passed to _generate_examples
#                     gen_kwargs={
#                         "filepath": corpora["train"]
#                     },
#                 ),
#                 datasets.SplitGenerator(
#                     name=datasets.Split.VALIDATION,
#                     # These kwargs will be passed to _generate_examples
#                     gen_kwargs={
#                         "filepath": corpora["dev"]
#                     },
#                 ),
#                 datasets.SplitGenerator(
#                     name=datasets.Split.TEST,
#                     # These kwargs will be passed to _generate_examples
#                     gen_kwargs={
#                         "filepath": corpora["test"]
#                     },
#                 ),
#             ]

#         else:
#             return [
#                 datasets.SplitGenerator(
#                     name=datasets.Split.TRAIN,
#                     # These kwargs will be passed to _generate_examples
#                     gen_kwargs={
#                         "filepath": corpora["train"]
#                     },
#                 ),
#                 datasets.SplitGenerator(
#                     name=datasets.Split.VALIDATION,
#                     # These kwargs will be passed to _generate_examples
#                     gen_kwargs={
#                         "filepath": corpora["dev"]
#                     },
#                 ),
#                 datasets.SplitGenerator(
#                     name=datasets.Split.TEST,
#                     # These kwargs will be passed to _generate_examples
#                     gen_kwargs={
#                         "filepath": corpora["test"]
#                     },
#                 ),
#             ]
    
#     def _generate_examples(self, filepath: List[str]):

#         """ Yields Controlled Text Reduction examples from a csv file. Each instance contains the document, the summary and the pre-selected spans."""

#         # merge annotations from sections 
#         df = pd.read_csv(filepath, index_col=False)
#         for counter, dic in enumerate(df.to_dict('records')):
#             columns_to_load_into_object = ["doc_text", "summary_text", "highlight_spans"]
#             for key in columns_to_load_into_object:
#                 dic[key] = eval(dic[key])
#             yield counter, dic





#################################################################################################################################################






# coding=utf-8
# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# 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 Controlled Text Reduction dataset."""


import datasets
from pathlib import Path
from typing import List
import pandas as pd
from dataclasses import dataclass

_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 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/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 = {
    "DUC-2001-2002": {
        "train": "https://media.githubusercontent.com/media/lovodkin93/Controlled_Text_Reduction/main/data/train_DUC-2001-2002.csv",
        "dev": "https://media.githubusercontent.com/media/lovodkin93/Controlled_Text_Reduction/main/data/dev_DUC-2001-2002.csv",
        "test": "https://media.githubusercontent.com/media/lovodkin93/Controlled_Text_Reduction/main/data/test_DUC-2001-2002.csv",
    },  
    "CNN-DM": {
        "train": "https://media.githubusercontent.com/media/lovodkin93/Controlled_Text_Reduction/main/data/train_CNNDM.csv",
        "dev": "",
        "test": "",
    },    
}

# _URLs = {
#         "dev_DUC-2001-2002": "https://media.githubusercontent.com/media/lovodkin93/Controlled_Text_Reduction/main/data/dev_DUC-2001-2002.csv",
#         "test_DUC-2001-2002": "https://media.githubusercontent.com/media/lovodkin93/Controlled_Text_Reduction/main/data/test_DUC-2001-2002.csv",
#         "train_DUC-2001-2002": "https://media.githubusercontent.com/media/lovodkin93/Controlled_Text_Reduction/main/data/train_DUC-2001-2002.csv"   
# }


COLUMNS = ["doc_text", "summary_text", "highlight_spans"]


# _URLs = {
#     "DUC-2001-2002": {
#         "dev": "https://github.com/lovodkin93/Controlled_Text_Reduction/tree/main/data/dev_DUC-2001-2002.csv",
#         "test": "https://github.com/lovodkin93/Controlled_Text_Reduction/tree/main/data/test_DUC-2001-2002.csv",
#         "train": "https://github.com/lovodkin93/Controlled_Text_Reduction/tree/main/data/train_DUC-2001-2002.csv"
#     },  
#     "CNN-DM": {
#         "train": "https://github.com/lovodkin93/Controlled_Text_Reduction/tree/main/data/train_CNNDM.csv",
#         "dev": "https://github.com/lovodkin93/Controlled_Text_Reduction/tree/main/data/dev_DUC-2001-2002.csv",
#         "test": "https://github.com/lovodkin93/Controlled_Text_Reduction/tree/main/data/test_DUC-2001-2002.csv",
#     },    
# }


@dataclass
class ControlledTextReductionConfig(datasets.BuilderConfig):
    """ Allow the loader to re-distribute the original dev and test splits between train, dev and test. """
    data_source: str = "DUC-2001-2002" # "DUC-2001-2002" or "CNN-DM"





class ControlledTectReduction(datasets.GeneratorBasedBuilder):
    """Controlled Text Reduction: dataset for the Controlled Text Reduction task ().
    Each data point consists of a document, a summary, and a list of spans of the document that are the pre-selected content whose summary is the summary"""


    VERSION = datasets.Version("1.0.0")

    BUILDER_CONFIG_CLASS = ControlledTextReductionConfig

    BUILDER_CONFIGS = [
        ControlledTextReductionConfig(
            name="DUC-2001-2002", 
            version=VERSION, 
            description="This provides the Controlled Text Reduction dataset extracted from the DUC 2001-2002 Single Document Summarization benchmark",
            data_source="DUC-2001-2002"
        ),
        ControlledTextReductionConfig(
            name="CNN-DM", 
            version=VERSION, 
            description="This provides the Controlled Text Reduction dataset extracted from the CNN-DM dataset (the train split)",
            data_source="CNN-DM"
        )
    ]

    DEFAULT_CONFIG_NAME = (
        "DUC-2001-2002"  # It's not mandatory to have a default configuration. Just use one if it make sense.
    )

    def _info(self):
        features = datasets.Features(
            {
                "doc_text": datasets.Value("string"),
                "summary_text": datasets.Value("string"),
                "highlight_spans": datasets.Value("string"),
            }
        )
        return datasets.DatasetInfo(
            # This is the description that will appear on the datasets page.
            description=_DESCRIPTION,
            # This defines the different columns of the dataset and their types
            features=features,  # Here we define them above because they are different between the two configurations
            # If there's a common (input, target) tuple from the features,
            # specify them here. They'll be used if as_supervised=True in
            # builder.as_dataset.
            supervised_keys=None,
            # Homepage of the dataset for documentation
            homepage=_HOMEPAGE,
            # License for the dataset if available
            license=_LICENSE,
            # Citation for the dataset
            citation=_CITATION,
        )

            
    def _split_generators(self, dl_manager: datasets.utils.download_manager.DownloadManager):
        """Returns SplitGenerators."""            
        
        URLs = _URLs[self.config.data_source]
        # Download and prepare all files - keep same structure as URLs 
        corpora = {section:  Path(dl_manager.download_and_extract(URLs[section])) 
                   for section in URLs} 
        
        if self.config.data_source=="CNN-DM":
            return [
                    datasets.SplitGenerator(
                        name=datasets.Split.TRAIN,
                        # These kwargs will be passed to _generate_examples
                        gen_kwargs={
                            "filepath": corpora["train"]
                        },
                    ),
                ]
        else:

            return [
                    datasets.SplitGenerator(
                        name=datasets.Split.TRAIN,
                        # These kwargs will be passed to _generate_examples
                        gen_kwargs={
                            "filepath": corpora["train"]
                        },
                    ),
                    datasets.SplitGenerator(
                        name=datasets.Split.VALIDATION,
                        # These kwargs will be passed to _generate_examples
                        gen_kwargs={
                            "filepath": corpora["dev"]
                        },
                    ),
                    datasets.SplitGenerator(
                        name=datasets.Split.TEST,
                        # These kwargs will be passed to _generate_examples
                        gen_kwargs={
                            "filepath": corpora["test"]
                        },
                    ),
                ]

















    
    def _generate_examples(self, filepath: List[str]):

        """ Yields Controlled Text Reduction examples from a csv file. Each instance contains the document, the summary and the pre-selected spans."""

        # merge annotations from sections 
        df = pd.read_csv(filepath)
        for counter, dic in enumerate(df.to_dict('records')):
            columns_to_load_into_object = ["doc_text", "summary_text", "highlight_spans"]
            # for key in columns_to_load_into_object:
            #     dic[key] = eval(dic[key])
            yield counter, dic