Datasets:
Multilinguality:
multilingual
Size Categories:
1M<n<10M
Language Creators:
found
Annotations Creators:
found
Source Datasets:
original
ArXiv:
License:
cc-by-nc-sa-4.0
| """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"], | |
| } | |