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Controlled-Text-Reduction-dataset.py

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-# 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": "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",
-    },    
-}
-
-
-@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')):
-            yield counter, dic

DUC-2001-2002/controlled-text-reduction-dataset-test.parquet

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DUC-2001-2002/controlled-text-reduction-dataset-train.parquet

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DUC-2001-2002/controlled-text-reduction-dataset-validation.parquet

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README.md

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-# Controlled Text Reduction
-
-This dataset contains Controlled Text Reduction triplets - document-summary pairs, and the spans in the document that cover the summary.
-The task input is consists of a document with pre-selected spans in it ("highlights"). The output is a text covering all and only the highlighted content.
-
-The script downloads the data from the original [GitHub repository](https://github.com/lovodkin93/Controlled_Text_Reduction). 
-
-### Format
-
-The dataset contains the following important features:
- 
-* `doc_text` - the input text. 
-* `summary_text` - the output text. 
-* `highlight_spans` - the spans in the input text (the doc_text) that lead to the output text (the summary_text). 
-
-```json
-{'doc_text': 'The motion picture industry\'s most coveted award...with 32.',
-  'summary_text': 'The Oscar, created 60 years ago by MGM...awarded person (32).',
-  'highlight_spans':'[[0, 48], [50, 55], [57, 81], [184, 247], ..., [953, 975], [1033, 1081]]'}
-```
-where for each document-summary pair, we save the spans in the input document that lead to the summary. 
-
-Notice that the dataset consists of two subsets:
-1. `DUC-2001-2002` - which is further divided into 3 splits (train, validation and test).
-2. `CNN-DM` - which has a single split.
-
-Citation
-========
-If you find the Controlled Text Reduction dataset useful in your research, please cite the following paper:
-```
-@misc{https://doi.org/10.48550/arxiv.2210.13449,
-  doi = {10.48550/ARXIV.2210.13449},
-  url = {https://arxiv.org/abs/2210.13449},
-  author = {Slobodkin, Aviv and Roit, Paul and Hirsch, Eran and Ernst, Ori and Dagan, Ido},
-  keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
-  title = {Controlled Text Reduction},
-  publisher = {arXiv},
-  year = {2022},
-  copyright = {Creative Commons Zero v1.0 Universal}
-}
-
-```