id_liputan6 / id_liputan6.py
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# coding=utf-8
# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Large-scale Indonesian Summarization Dataset"""
import glob
import json
import os
import re
from pathlib import Path
import datasets
logger = datasets.logging.get_logger(__name__)
_CITATION = """\
@inproceedings{id_liputan6,
author = {Fajri Koto, Jey Han Lau, Timothy Baldwin},
title = {Liputan6: A Large-scale Indonesian Dataset for Text Summarization},
year = {2020},
url = {https://arxiv.org/abs/2011.00679},
}
"""
_DESCRIPTION = """\
In this paper, we introduce a large-scale Indonesian summarization dataset. We harvest articles from this http URL,
an online news portal, and obtain 215,827 document-summary pairs. We leverage pre-trained language models to develop
benchmark extractive and abstractive summarization methods over the dataset with multilingual and monolingual
BERT-based models. We include a thorough error analysis by examining machine-generated summaries that have
low ROUGE scores, and expose both issues with ROUGE it-self, as well as with extractive and abstractive
summarization models.
"""
_HOMEPAGE = "https://arxiv.org/abs/2011.00679"
_LICENSE = ""
class IdLiputan6Config(datasets.BuilderConfig):
"""BuilderConfig for IdLiputan6"""
def __init__(self, **kwargs):
"""BuilderConfig for IdLiputan6.
Args:
**kwargs: keyword arguments forwarded to super.
"""
super(IdLiputan6Config, self).__init__(**kwargs)
class IdLiputan6(datasets.GeneratorBasedBuilder):
VERSION = datasets.Version("1.0.0")
BUILDER_CONFIGS = [
IdLiputan6Config(
name="canonical",
version=VERSION,
description="Canonical Liputan6 dataset",
),
IdLiputan6Config(
name="xtreme",
version=VERSION,
description="Xtreme Liputan6 dataset",
),
]
@property
def manual_download_instructions(self):
return """\
You need to manually request the liputan6 dataset using the form in https://github.com/fajri91/sum_liputan6/
and uncompress it. The liputan6 dataset can then be loaded using the following command
`datasets.load_dataset("id_liputan6", 'canonical', data_dir="<path/to/uncompressed_folder>")` or
`datasets.load_dataset("id_liputan6", 'xtreme', data_dir="<path/to/uncompressed_folder>")`.
"""
def _info(self):
features = datasets.Features(
{
"id": datasets.Value("string"),
"url": datasets.Value("string"),
"clean_article": datasets.Value("string"),
"clean_summary": datasets.Value("string"),
"extractive_summary": datasets.Value("string"),
}
)
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=features,
supervised_keys=None,
homepage=_HOMEPAGE,
license=_LICENSE,
citation=_CITATION,
)
def _split_generators(self, dl_manager):
data_dir = os.path.abspath(os.path.expanduser(dl_manager.manual_dir))
if not os.path.exists(data_dir):
raise FileNotFoundError(
f"{data_dir} does not exist. Make sure you insert a manual dir via `datasets.load_dataset('id_liputan6', "
"'canonical', data_dir=...)`. Manual download instructions:\n{self.manual_download_instructions}"
)
split_generators = [
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={
"article_dir": os.path.join(data_dir, f"{self.config.name}/dev"),
"split": "dev",
},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={
"article_dir": os.path.join(data_dir, f"{self.config.name}/test"),
"split": "test",
},
),
]
if self.config.name == "canonical":
split_generators.append(
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"article_dir": os.path.join(data_dir, f"{self.config.name}/train"),
"split": "train",
},
)
)
return split_generators
def _generate_examples(self, article_dir, split):
detokenizers = [
[re.compile(r"([Ll])iputan6 . com "), r"\1iputan6.com"],
[re.compile(r" ([.,:])"), r"\1"],
[re.compile(r"\( ([^)]+) \)"), r"(\1)"],
[re.compile(r"\" ([^\"]+) \""), r'"\1"'],
[re.compile(r"\[ ([^]]+) ]"), r"[\1]"],
]
logger.info("⏳ Generating %s examples from = %s", split, article_dir)
guid = 0
for path in sorted(
glob.glob(os.path.join(article_dir, "**/*.json"), recursive=True), key=lambda p: int(Path(p).stem)
):
with open(path, encoding="utf-8") as f:
data = json.load(f)
clean_article = " ".join([" ".join(i) for i in data["clean_article"]])
for d in detokenizers:
clean_article = d[0].sub(d[1], clean_article)
clean_summary = " ".join([" ".join(i) for i in data["clean_summary"]])
for d in detokenizers:
clean_summary = d[0].sub(d[1], clean_summary)
extractive_summary = " ".join([" ".join(data["clean_article"][i]) for i in data["extractive_summary"]])
for d in detokenizers:
extractive_summary = d[0].sub(d[1], extractive_summary)
yield guid, {
"id": str(data["id"]),
"url": data["url"],
"clean_article": clean_article,
"clean_summary": clean_summary,
"extractive_summary": extractive_summary,
}
guid += 1