Datasets:

csebuetnlp
/

CrossSum

	"""CrossSum cross-lingual abstractive summarization dataset."""


	import json
	import os

	import datasets


	_CITATION = """\
	@article{hasan2021crosssum,
	author = {Tahmid Hasan and Abhik Bhattacharjee and Wasi Uddin Ahmad and Yuan-Fang Li and Yong-bin Kang and Rifat Shahriyar},
	title = {CrossSum: Beyond English-Centric Cross-Lingual Abstractive Text Summarization for 1500+ Language Pairs},
	journal = {CoRR},
	volume = {abs/2112.08804},
	year = {2021},
	url = {https://arxiv.org/abs/2112.08804},
	eprinttype = {arXiv},
	eprint = {2112.08804}
	}
	"""


	_DESCRIPTION = """\
	We present CrossSum, a large-scale dataset
	comprising 1.70 million cross-lingual article summary samples in 1500+ language-pairs
	constituting 45 languages. We use the multilingual XL-Sum dataset and align identical
	articles written in different languages via crosslingual retrieval using a language-agnostic
	representation model.
	"""

	_HOMEPAGE = "https://github.com/csebuetnlp/CrossSum"

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

	_URL = "https://huggingface.co/datasets/csebuetnlp/CrossSum/resolve/main/data/{}-{}_CrossSum.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 Crosssum(datasets.GeneratorBasedBuilder):

	BUILDER_CONFIGS = [
	datasets.BuilderConfig(
	name="{}-{}".format(src_lang, tgt_lang),
	version=datasets.Version("1.0.0")
	)
	for src_lang in _LANGUAGES
	for tgt_lang in _LANGUAGES
	]

	def _info(self):
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=datasets.Features(
	{
	"source_url": datasets.Value("string"),
	"target_url": 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_, {
	"source_url": data["source_url"],
	"target_url": data["target_url"],
	"summary": data["summary"],
	"text": data["text"],
	}