Datasets:

csebuetnlp
/

xlsum

	"""XL-Sum abstractive summarization dataset."""


	import json
	import os

	import datasets


	_CITATION = """\
	@inproceedings{hasan-etal-2021-xl,
	title = "{XL}-Sum: Large-Scale Multilingual Abstractive Summarization for 44 Languages",
	author = "Hasan, Tahmid and
	Bhattacharjee, Abhik and
	Islam, Md. Saiful and
	Mubasshir, Kazi and
	Li, Yuan-Fang and
	Kang, Yong-Bin and
	Rahman, M. Sohel and
	Shahriyar, Rifat",
	booktitle = "Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021",
	month = aug,
	year = "2021",
	address = "Online",
	publisher = "Association for Computational Linguistics",
	url = "https://aclanthology.org/2021.findings-acl.413",
	pages = "4693--4703",
	}
	"""


	_DESCRIPTION = """\
	We present XLSum, a comprehensive and diverse dataset comprising 1.35 million professionally
	annotated article-summary pairs from BBC, extracted using a set of carefully designed heuristics.
	The dataset covers 45 languages ranging from low to high-resource, for many of which no
	public dataset is currently available. XL-Sum is highly abstractive, concise,
	and of high quality, as indicated by human and intrinsic evaluation.
	"""

	_HOMEPAGE = "https://github.com/csebuetnlp/xl-sum"

	_LICENSE = "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0)"

	_URL = "https://huggingface.co/datasets/csebuetnlp/xlsum/resolve/main/data/{}_XLSum_v{}.tar.bz2"

	_LANGUAGES = [
	"oromo",
	"french",
	"amharic",
	"arabic",
	"azerbaijani",
	"bengali",
	"burmese",
	"chinese_simplified",
	"chinese_traditional",
	"welsh",
	"english",
	"kirundi",
	"gujarati",
	"hausa",
	"hindi",
	"igbo",
	"indonesian",
	"japanese",
	"korean",
	"kyrgyz",
	"marathi",
	"spanish",
	"scottish_gaelic",
	"nepali",
	"pashto",
	"persian",
	"pidgin",
	"portuguese",
	"punjabi",
	"russian",
	"serbian_cyrillic",
	"serbian_latin",
	"sinhala",
	"somali",
	"swahili",
	"tamil",
	"telugu",
	"thai",
	"tigrinya",
	"turkish",
	"ukrainian",
	"urdu",
	"uzbek",
	"vietnamese",
	"yoruba",
	]


	class Xlsum(datasets.GeneratorBasedBuilder):
	VERSION = datasets.Version("2.0.0")

	BUILDER_CONFIGS = [
	datasets.BuilderConfig(
	name="{}".format(lang),
	version=datasets.Version("2.0.0")
	)
	for lang in _LANGUAGES
	]

	def _info(self):
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=datasets.Features(
	{
	"id": datasets.Value("string"),
	"url": datasets.Value("string"),
	"title": datasets.Value("string"),
	"summary": datasets.Value("string"),
	"text": datasets.Value("string"),
	}
	),
	supervised_keys=None,
	homepage=_HOMEPAGE,
	citation=_CITATION,
	license=_LICENSE,
	version=self.VERSION,
	)

	def _split_generators(self, dl_manager):
	"""Returns SplitGenerators."""
	lang = str(self.config.name)
	url = _URL.format(lang, self.VERSION.version_str[:-2])

	data_dir = dl_manager.download_and_extract(url)
	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	gen_kwargs={
	"filepath": os.path.join(data_dir, lang + "_train.jsonl"),
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split.TEST,
	gen_kwargs={
	"filepath": os.path.join(data_dir, lang + "_test.jsonl"),
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split.VALIDATION,
	gen_kwargs={
	"filepath": os.path.join(data_dir, lang + "_val.jsonl"),
	},
	),
	]

	def _generate_examples(self, filepath):
	"""Yields examples as (key, example) tuples."""
	with open(filepath, encoding="utf-8") as f:
	for idx_, row in enumerate(f):
	data = json.loads(row)
	yield idx_, {
	"id": data["id"],
	"url": data["url"],
	"title": data["title"],
	"summary": data["summary"],
	"text": data["text"],
	}