Datasets:
Tasks:
Summarization
Languages:
French
Multilinguality:
monolingual
Size Categories:
10K<n<100K
Language Creators:
found
Annotations Creators:
found
Source Datasets:
original
ArXiv:
Tags:
License:
File size: 5,005 Bytes
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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.
"""OrangeSum dataset"""
import datasets
_CITATION = """\
@article{eddine2020barthez,
title={BARThez: a Skilled Pretrained French Sequence-to-Sequence Model},
author={Eddine, Moussa Kamal and Tixier, Antoine J-P and Vazirgiannis, Michalis},
journal={arXiv preprint arXiv:2010.12321},
year={2020}
}
"""
_DESCRIPTION = """\
The OrangeSum dataset was inspired by the XSum dataset. It was created by scraping the "Orange Actu" website: https://actu.orange.fr/. Orange S.A. is a large French multinational telecommunications corporation, with 266M customers worldwide. Scraped pages cover almost a decade from Feb 2011 to Sep 2020. They belong to five main categories: France, world, politics, automotive, and society. The society category is itself divided into 8 subcategories: health, environment, people, culture, media, high-tech, unsual ("insolite" in French), and miscellaneous.
Each article featured a single-sentence title as well as a very brief abstract, both professionally written by the author of the article. These two fields were extracted from each page, thus creating two summarization tasks: OrangeSum Title and OrangeSum Abstract.
"""
_URL_DATA = {
"abstract": "https://raw.githubusercontent.com/Tixierae/OrangeSum/main/data/docs/splits/abstract.tgz",
"title": "https://raw.githubusercontent.com/Tixierae/OrangeSum/main/data/docs/splits/title.tgz",
}
_DOCUMENT = "text"
_SUMMARY = "summary"
class OrangeSum(datasets.GeneratorBasedBuilder):
"""OrangeSum: a french abstractive summarization dataset"""
VERSION = datasets.Version("1.1.0")
BUILDER_CONFIGS = [
datasets.BuilderConfig(name="abstract", description="Abstracts used as summaries", version=VERSION),
datasets.BuilderConfig(name="title", description="Titles used as summaries", version=VERSION),
]
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
_DOCUMENT: datasets.Value("string"),
_SUMMARY: datasets.Value("string"),
}
),
supervised_keys=(_DOCUMENT, _SUMMARY),
homepage="https://github.com/Tixierae/OrangeSum/",
citation=_CITATION,
)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
archive = dl_manager.download(_URL_DATA[self.config.name])
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
# These kwargs will be passed to _generate_examples
gen_kwargs={
"source_files": dl_manager.iter_archive(archive),
"target_files": dl_manager.iter_archive(archive),
"split": "train",
},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
# These kwargs will be passed to _generate_examples
gen_kwargs={
"source_files": dl_manager.iter_archive(archive),
"target_files": dl_manager.iter_archive(archive),
"split": "test",
},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
# These kwargs will be passed to _generate_examples
gen_kwargs={
"source_files": dl_manager.iter_archive(archive),
"target_files": dl_manager.iter_archive(archive),
"split": "valid",
},
),
]
def _generate_examples(self, source_files, target_files, split):
"""Yields examples."""
expected_source_path = f"{self.config.name}/{split}.source"
expected_target_path = f"{self.config.name}/{split}.target"
for source_path, f_source in source_files:
if source_path == expected_source_path:
for target_path, f_target in target_files:
if target_path == expected_target_path:
for idx, (document, summary) in enumerate(zip(f_source, f_target)):
yield idx, {_DOCUMENT: document.decode("utf-8"), _SUMMARY: summary.decode("utf-8")}
break
break
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