update the dataset loading script

controlled_text_reduction.py

@@ -0,0 +1,204 @@
+# 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://huggingface.co/datasets/lovodkin93/Controlled-Text-Reduction-dataset"
+
+_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://huggingface.co/datasets/lovodkin93/Controlled-Text-Reduction-dataset/blob/main/data/dev_DUC-2001-2002.csv",
+        "test": "https://huggingface.co/datasets/lovodkin93/Controlled-Text-Reduction-dataset/blob/main/data/test_DUC-2001-2002.csv",
+        "train": "https://huggingface.co/datasets/lovodkin93/Controlled-Text-Reduction-dataset/blob/main/data/train_DUC-2001-2002.csv"
+    },  
+    "CNN-DM": {
+        "train": "https://huggingface.co/datasets/lovodkin93/Controlled-Text-Reduction-dataset/blob/main/data/train_CNNDM.csv"
+    },    
+}
+
+
+
+
+
+
+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_CONFIGS = [
+        datasets.BuilderConfig(
+            name="DUC-2001-2002", 
+            version=VERSION, 
+            description="This provides the Controlled Text Reduction dataset extracted from the DUC 2001-2002 Single Document Summarization benchmark"
+        ),
+        datasets.BuilderConfig(
+            name="CNN-DM", 
+            version=VERSION, 
+            description="This provides the Controlled Text Reduction dataset extracted from the CNN-DM dataset (the train split)"
+        )
+    ]
+
+    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.name]
+        # 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.name=="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, 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