Datasets:

GEM
/

mlsum

	import json
	import os

	import datasets

	_CITATION = """\
	@article{scialom2020mlsum,
	title={MLSUM: The Multilingual Summarization Corpus},
	author={Scialom, Thomas and Dray, Paul-Alexis and Lamprier, Sylvain and Piwowarski, Benjamin and Staiano, Jacopo},
	journal={arXiv preprint arXiv:2004.14900},
	year={2020}
	}
	"""

	_DESCRIPTION = """\
	This is the MLSUM subset of the GEM benchmark. MLSUM is the first large-scale MultiLingual SUMmarization dataset.
	Obtained from online newspapers, it contains 1.5M+ article/summary pairs in five different languages -- namely, French, German, Spanish, Russian, Turkish.
	Together with English newspapers from the popular CNN/Daily mail dataset, the collected data form a large scale multilingual dataset which can enable new research directions for the text summarization community.
	We report cross-lingual comparative analyses based on state-of-the-art systems.
	These highlight existing biases which motivate the use of a multi-lingual dataset.
	"""
	_URL = "https://gitlab.lip6.fr/scialom/mlsum_data/-/raw/master/MLSUM/"
	_LANG = ["de", "es"]
	_URLs = {
	"de": {
	"train": "https://gitlab.lip6.fr/scialom/mlsum_data/-/raw/master/MLSUM/de_train.zip",
	"validation": "https://gitlab.lip6.fr/scialom/mlsum_data/-/raw/master/MLSUM/de_val.zip",
	"test": "https://gitlab.lip6.fr/scialom/mlsum_data/-/raw/master/MLSUM/de_test.zip",
	"bad_ids": "https://storage.googleapis.com/huggingface-nlp/datasets/gem/gem_mlsum_bad_ids_fixed.json",
	"challenge_set": "https://storage.googleapis.com/huggingface-nlp/datasets/gem/gem_challenge_sets/mlsum_de.zip",
	},
	"es": {
	"train": "https://gitlab.lip6.fr/scialom/mlsum_data/-/raw/master/MLSUM/es_train.zip",
	"validation": "https://gitlab.lip6.fr/scialom/mlsum_data/-/raw/master/MLSUM/es_val.zip",
	"test": "https://gitlab.lip6.fr/scialom/mlsum_data/-/raw/master/MLSUM/es_test.zip",
	"bad_ids": "https://storage.googleapis.com/huggingface-nlp/datasets/gem/gem_mlsum_bad_ids_fixed.json",
	"challenge_set": "https://storage.googleapis.com/huggingface-nlp/datasets/gem/gem_challenge_sets/mlsum_es.zip",
	},
	}


	class Mlsum(datasets.GeneratorBasedBuilder):

	BUILDER_CONFIGS = [
	datasets.BuilderConfig(
	name=lang,
	version=datasets.Version("1.0.0"),
	description="",
	)
	for lang in _LANG
	]

	def _info(self):
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=datasets.Features(
	{
	"gem_id": datasets.Value("string"),
	"gem_parent_id": datasets.Value("string"),
	"text": datasets.Value("string"),
	"topic": datasets.Value("string"),
	"url": datasets.Value("string"),
	"title": datasets.Value("string"),
	"date": datasets.Value("string"),
	"target": datasets.Value("string"),
	"references": [datasets.Value("string")],
	}
	),
	supervised_keys=None,
	homepage="",
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	"""Returns SplitGenerators."""
	dl_dir = dl_manager.download_and_extract(_URLs[self.config.name])
	lang = str(self.config.name)
	challenge_sets = [
	("challenge_train_sample", f"train_mlsum_{lang}_RandomSample500.json"),
	("challenge_validation_sample", f"validation_mlsum_{lang}_RandomSample500.json"),
	("challenge_test_covid", f"{lang}_test_covid19_cleaned.jsonl"),
	]
	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	gen_kwargs={
	"filepath": os.path.join(dl_dir["train"], lang + "_train.jsonl"),
	"split": "train",
	"lang": lang,
	"filepaths": dl_dir["bad_ids"],
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split.VALIDATION,
	gen_kwargs={
	"filepath": os.path.join(dl_dir["validation"], lang + "_val.jsonl"),
	"split": "validation",
	"lang": lang,
	"filepaths": dl_dir["bad_ids"],
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split.TEST,
	gen_kwargs={
	"filepath": os.path.join(dl_dir["test"], lang + "_test.jsonl"),
	"split": "test",
	"lang": lang,
	"filepaths": dl_dir["bad_ids"],
	},
	),
	] + [
	datasets.SplitGenerator(
	name=challenge_split,
	gen_kwargs={
	"filepath": os.path.join(dl_dir["challenge_set"], f"mlsum_{self.config.name}", filename),
	"split": challenge_split,
	},
	)
	for challenge_split, filename in challenge_sets
	]

	def _generate_examples(self, filepath, split, filepaths=None, lang=None):
	"""Yields examples."""
	if split in ["train", "validation", "test", "challenge_test_covid"]:
	if split == "challenge_test_covid":
	bad_ids = {}
	else:
	bad_ids_dct = json.load(open(filepaths, encoding="utf-8"))
	bad_ids = dict((bad_url, True) for _, bad_url in bad_ids_dct[f"{lang}-{split}"])
	with open(filepath, encoding="utf-8") as f:
	id_ = -1
	for line in f:
	data = json.loads(line)
	if data["url"] in bad_ids:
	continue
	else:
	id_ += 1
	yield id_, {
	"gem_id": f"mlsum_{self.config.name}-{split}-{id_}",
	"gem_parent_id": f"mlsum_{self.config.name}-{split}-{id_}",
	"text": data["text"],
	"target": data["summary"],
	"references": [] if split == "train" else [data["summary"]],
	"topic": data["topic"],
	"url": data["url"],
	"title": data["title"],
	"date": data["date"],
	}
	else:
	exples = json.load(open(filepath, encoding="utf-8"))
	if isinstance(exples, dict):
	assert len(exples) == 1, "multiple entries found"
	exples = list(exples.values())[0]
	for id_, exple in enumerate(exples):
	if len(exple) == 0:
	continue
	exple["gem_parent_id"] = exple["gem_id"]
	exple["gem_id"] = f"mlsum_{self.config.name}-{split}-{id_}"
	yield id_, exple