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"""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)"
_DATA_PATH = "data/{}_XLSum_v{}.tar.bz2" # relative path to the data
_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)
data_path = _DATA_PATH.format(lang, self.VERSION.version_str[:-2])
# we download and use dl_manager.iter_archive to be able to load the dataset in streaming mode
archive = dl_manager.download(data_path)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"filepath": lang + "_train.jsonl",
"files": dl_manager.iter_archive(archive),
},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={
"filepath": lang + "_test.jsonl",
"files": dl_manager.iter_archive(archive),
},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={
"filepath": lang + "_val.jsonl",
"files": dl_manager.iter_archive(archive),
},
),
]
def _generate_examples(self, filepath, files):
"""Yields examples as (key, example) tuples."""
for path, f in files:
if path == filepath:
for idx_, row in enumerate(f):
data = json.loads(row.decode("utf-8"))
yield idx_, {
"id": data["id"],
"url": data["url"],
"title": data["title"],
"summary": data["summary"],
"text": data["text"],
}
break
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