Hugging Face's logo

Datasets:
csebuetnlp
/
CrossSum

Tasks:
Summarization
Sub-tasks:
news-articles-summarization
Languages:
Amharic
Arabic
Azerbaijani
Size:
1M<n<10M
ArXiv:
License:
Dataset card Files Community
9
CrossSum
File size: 4,255 Bytes 
73b5e30
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
ca828be
 
73b5e30
 
 
 
 
 
f2f7ce8
73b5e30
 
 
 
 
f2f7ce8
73b5e30
 
 
 
 
f2f7ce8
73b5e30
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
 
"""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_pairs = str(self.config.name)
        url = _URL.format(*lang_pairs.split("-"))

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