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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.
"""WikiLingua."""
import json
import datasets
# Find for instance the citation on arxiv or on the dataset repo/website
_CITATION = """\
@inproceedings{ladhak-etal-2020-wikilingua,
title = "{W}iki{L}ingua: A New Benchmark Dataset for Cross-Lingual Abstractive Summarization",
author = "Ladhak, Faisal and
Durmus, Esin and
Cardie, Claire and
McKeown, Kathleen",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.findings-emnlp.360",
doi = "10.18653/v1/2020.findings-emnlp.360",
pages = "4034--4048",
}
"""
_DESCRIPTION = """\
WikiLingua is a large-scale multilingual dataset for the evaluation of
cross-lingual 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):
"""WikiLingua dataset."""
VERSION = datasets.Version("1.1.1")
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
# 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"])}
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