|
"""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"], |
|
} |
|
|
