wiki_lingua.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.
"""TODO: Add a description here."""


import json

import datasets


# Find for instance the citation on arxiv or on the dataset repo/website
_CITATION = """\
@article{ladhak-wiki-2020,
  title   = {WikiLingua: A New Benchmark Dataset for Multilingual Abstractive Summarization},
  authors = {Faisal Ladhak, Esin Durmus, Claire Cardie and Kathleen McKeown},
  journal = {arXiv preprint arXiv:2010.03093},
  year    = {2020},
  url     = {https://arxiv.org/abs/2010.03093}
}
"""

_DESCRIPTION = """\
WikiLingua is a large-scale multilingual dataset for the evaluation of
crosslingual abstractive summarization systems. The dataset includes ~770k
article and summary pairs in 18 languages from WikiHow. The gold-standard
article-summary alignments across languages was done by aligning the images
that are used to describe each how-to step in an article.
"""

_HOMEPAGE = "https://github.com/esdurmus/Wikilingua"

_LICENSE = "CC BY-NC-SA 3.0"

# Download link
_URL = "data/{language}.jsonl.gz"
_LANGUAGES = [
    "arabic",
    "chinese",
    "czech",
    "dutch",
    "english",
    "french",
    "german",
    "hindi",
    "indonesian",
    "italian",
    "japanese",
    "korean",
    "portuguese",
    "russian",
    "spanish",
    "thai",
    "turkish",
    "vietnamese",
]


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

    VERSION = datasets.Version("1.1.1")

    # 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')
    # data = datasets.load_dataset('my_dataset', 'second_domain')
    BUILDER_CONFIGS = [
        datasets.BuilderConfig(
            name=lang,
            version=datasets.Version("1.1.1"),
            description=f"A subset of article-summary in {lang.capitalize()}",
        )
        for lang in _LANGUAGES
    ]

    DEFAULT_CONFIG_NAME = "english"

    def _info(self):
        if self.config.name == "english":
            features = datasets.Features(
                {
                    "url": datasets.Value("string"),
                    "article": datasets.Sequence(
                        {
                            "section_name": datasets.Value("string"),
                            "document": datasets.Value("string"),
                            "summary": datasets.Value("string"),
                        }
                    ),
                }
            )
        else:
            features = datasets.Features(
                {
                    "url": datasets.Value("string"),
                    "article": datasets.Sequence(
                        {
                            "section_name": datasets.Value("string"),
                            "document": datasets.Value("string"),
                            "summary": datasets.Value("string"),
                            "english_url": datasets.Value("string"),
                            "english_section_name": 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):
        """Returns SplitGenerators."""
        filepath = dl_manager.download_and_extract(_URL.format(language=self.config.name))
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                # These kwargs will be passed to _generate_examples
                gen_kwargs={
                    "filepath": filepath,
                },
            ),
        ]

    def _process_article(self, article):
        """Parse the article and convert into list of dict"""
        processed_article = []
        for key, value in article.items():
            row = {"section_name": key, "document": value["document"], "summary": value["summary"]}

            if self.config.name != "english":
                row["english_url"] = value["english_url"]
                row["english_section_name"] = value["english_section_name"]
            processed_article.append(row)

        return processed_article

    def _generate_examples(self, filepath):
        """Yields examples."""
        with open(filepath, "rb") as f:
            for id_, line in enumerate(f):
                row = json.loads(line)
                yield id_, {"url": row["url"], "article": self._process_article(row["article"])}