Datasets:
id_liputan6

	# 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