Controlled-Text-Reduction-dataset.py

# # 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 = (
#         "default"  # 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 QA-Discourse dataset (Pyatkin et. al., ACL 2020)."""


import datasets
from pathlib import Path
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}
}"""


_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
"""

_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/)."""


_URLs = {
    "wikinews.train": "https://github.com/ValentinaPy/QADiscourse/raw/master/Dataset/wikinews_train.tsv",
    "wikinews.dev": "https://github.com/ValentinaPy/QADiscourse/raw/master/Dataset/wikinews_dev.tsv",
    "wikinews.test": "https://github.com/ValentinaPy/QADiscourse/raw/master/Dataset/wikinews_test.tsv",
    "wikipedia.train": "https://github.com/ValentinaPy/QADiscourse/raw/master/Dataset/wikipedia_train.tsv",
    "wikipedia.dev": "https://github.com/ValentinaPy/QADiscourse/raw/master/Dataset/wikipedia_dev.tsv",
    "wikipedia.test": "https://github.com/ValentinaPy/QADiscourse/raw/master/Dataset/wikipedia_test.tsv",      
}

COLUMNS = ['qasrl_id', 'sentence', 'worker_id', 'full_question', 'full_answer',
       'question_start', 'question_aux', 'question_body', 'answer',
       'untokenized sentence', 'target indices for untok sent']


# TODO: Name of the dataset usually match the script name with CamelCase instead of snake_case
class QaDiscourse(datasets.GeneratorBasedBuilder):
    """QA-Discourse: Discourse Relations as Question-Answer Pairs.  """

    VERSION = datasets.Version("1.0.2")

    BUILDER_CONFIGS = [
        datasets.BuilderConfig(
            name="plain_text", version=VERSION, description="This provides the QA-Discourse dataset"
        ),
    ]

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

    def _info(self):
        features = datasets.Features(
            {
                "sentence": datasets.Value("string"),
                "sent_id": datasets.Value("string"),
                "question": datasets.Sequence(datasets.Value("string")),
                "answers": datasets.Sequence(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."""            
        
        # Download and prepare all files - keep same structure as _URLs 
        corpora = {section:  Path(dl_manager.download_and_extract(_URLs[section])) 
                   for section in _URLs} 
            
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                # These kwargs will be passed to _generate_examples
                gen_kwargs={
                    "filepaths": [corpora["wikinews.train"], 
                                  corpora["wikipedia.train"]],
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.VALIDATION,
                # These kwargs will be passed to _generate_examples
                gen_kwargs={
                    "filepaths": [corpora["wikinews.dev"], 
                                  corpora["wikipedia.dev"]],
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.TEST,
                # These kwargs will be passed to _generate_examples
                gen_kwargs={
                    "filepaths": [corpora["wikinews.test"], 
                                  corpora["wikipedia.test"]],
                },
            ),
        ]
    
    def _generate_examples(self, filepaths: List[str]):

        """ 
        Yields QA-Discourse examples from a tsv file.
        Sentences with no QAs will yield an ``empty QA'' record, where both 'question' and 'answers' are empty lists.
        """

        # merge annotations from sections 
        df = pd.concat([pd.read_csv(fn, sep='\t', error_bad_lines=False) for fn in filepaths]).reset_index(drop=True) 
        df = df.applymap(str) # must turn all values to strings explicitly to avoid type errors
        for counter, row in df.iterrows():
            # Prepare question (3 "slots" and question mark)
            question = [row.question_start, row.question_aux, row.question_body.rstrip('?'), '?']
            answer = [row.answer]
            if row.question_start == "_":    # sentence has no QAs
                question = []
                answer = []
            
            yield counter, {
                "sentence": row.sentence,
                "sent_id": row.qasrl_id,
                "question": question,
                "answers": answer,
            }