squality.py

# 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.
# TODO: Address all TODOs and remove all explanatory comments
"""TODO: Add a description here."""
""" Dataset loading script for SQuALITY, an abstractive summarization dataset that is
* long document: 3k-6k words
* question-focused: 5/doc
* multi-reference 4/question
 """

import os
import csv
import json

import datasets


_CITATION = """\
@article{wang2022squality,
  title={{SQ}u{ALITY}: Building a Long-Document Summarization Dataset the Hard Way},
  author={Wang, Alex and Pang, Richard Yuanzhe and Chen, Angelica and Phang, Jason and Bowman, Samuel R.},
  journal={arXiv preprint 2205.11465},
  year={2022}
}
"""

# TODO: Add description of the dataset here
# You can copy an official description
_DESCRIPTION = """\
This new dataset is designed to solve this great NLP task and is crafted with a lot of care.
"""

_HOMEPAGE = "ihttps://github.com/nyu-mll/SQuALITY"

_LICENSE = "CC BY"

# TODO: Add link to the official dataset URLs here
# The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
#_URLS = {
#    "first_domain": "https://huggingface.co/great-new-dataset-first_domain.zip",
#    "second_domain": "https://huggingface.co/great-new-dataset-second_domain.zip",
#}


class SQuALITYDataset(datasets.GeneratorBasedBuilder):
    """TODO: Short description of my dataset."""

    VERSION = datasets.Version("1.1.0")

    # This is an example of a dataset with multiple configurations.
    # If you don't want/need to define several sub-sets in your dataset,
    # just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.

    # If you need to make complex sub-parts in the datasets with configurable options
    # You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
    # BUILDER_CONFIG_CLASS = MyBuilderConfig

    # You will be able to load one or the other configurations in the following list with
    # data = datasets.load_dataset('my_dataset', 'first_domain')
    BUILDER_CONFIGS = [
        datasets.BuilderConfig(name="squality-v1", version=datasets.Version("1.0.0"), description="SQUALITY v1.0, containing 100 stories (2000 summaries)"),
        datasets.BuilderConfig(name="squality-v1.1", version=VERSION, description="SQuALITY version v1.1, expands on v1.0 by adding 27 stories (540 summaries)"),
    ]

    DEFAULT_CONFIG_NAME = "squality-v1.1"  # It's not mandatory to have a default configuration. Just use one if it make sense.

    def _info(self):
        # This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset

        #if self.config.name == "first_domain":  # This is the name of the configuration selected in BUILDER_CONFIGS above
        #    features = datasets.Features(
        #        {
        #            "sentence": datasets.Value("string"),
        #            "option1": datasets.Value("string"),
        #            "answer": datasets.Value("string")
        #            # These are the features of your dataset like images, labels ...
        #        }
        #    )

        features = datasets.Features(
	    {
		"document": datasets.Value("string"),
		"question": datasets.Value("string"),
		"summary": datasets.Value("string")
		# These are the features of your dataset like images, labels ...
	    }
	)

        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,
            # If there's a common (input, target) tuple from the features,
            # uncomment supervised_keys line below and
            # specify them. They'll be used if as_supervised=True in builder.as_dataset.
            # supervised_keys=("sentence", "label"),
            # 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):
        # This method is tasked with downloading/extracting the data and
        # defining the splits depending on the configuration
        # If several configurations are possible (listed in BUILDER_CONFIGS),
        # the configuration selected by the user is in self.config.name

        if self.config.name == "squality-v1":
            data_dir = "data/v1"
        elif self.config.name == "squality-v1.1":
            data_dir = "data/v1-1"

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

    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
    def _generate_examples(self, filepath, split):
        # This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
        # The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
        with open(filepath, encoding="utf-8") as f:
            for row in enumerate(f):
                # fields
                # * metadata
                # * document
                # * questions
                story = json.loads(row)
                for question in story['questions']:
                    # fields
                    # * question_text
                    # * question_number
                    # * responses
                    key = question['gem_id']

                    # for the test split, yield all references at once
                    # to easily compute multi-reference metrics
                    if split == "test":
                        yield key, {
                            'document': story['document'],
                            'question': question['question_text'],
                            'summary': [r['response_text'] for r in question['responses']]
                        }

                    else:
                        for response in question['responses']:
                            # fields
                            # * uid
                            # * worker_uid
                            # * response_text
                            yield key, {
                                'document': story['document'],
                                'question': question['question_text'],
                                'summary': response['response_text']
                            }