Datasets:
multi_news

	# coding=utf-8
	# Copyright 2020 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors.
	#
	# 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.

	# Lint as: python3
	"""Multi-News dataset."""

	import datasets


	_HOMEPAGE = "https://github.com/Alex-Fabbri/Multi-News"

	_LICENSE = "For non-commercial research and educational purposes only"

	_CITATION = """
	@misc{alex2019multinews,
	title={Multi-News: a Large-Scale Multi-Document Summarization Dataset and Abstractive Hierarchical Model},
	author={Alexander R. Fabbri and Irene Li and Tianwei She and Suyi Li and Dragomir R. Radev},
	year={2019},
	eprint={1906.01749},
	archivePrefix={arXiv},
	primaryClass={cs.CL}
	}
	"""

	_DESCRIPTION = """
	Multi-News, consists of news articles and human-written summaries
	of these articles from the site newser.com.
	Each summary is professionally written by editors and
	includes links to the original articles cited.

	There are two features:
	- document: text of news articles seperated by special token "\|\|\|\|\|".
	- summary: news summary.
	"""

	_REPO = "https://huggingface.co/datasets/multi_news/resolve/main/data"
	_URLs = {
	"train": [
	f"{_REPO}/train.src.cleaned",
	f"{_REPO}/train.tgt",
	],
	"val": [
	f"{_REPO}/val.src.cleaned",
	f"{_REPO}/val.tgt",
	],
	"test": [
	f"{_REPO}/test.src.cleaned",
	f"{_REPO}/test.tgt",
	],
	}

	_DOCUMENT = "document"
	_SUMMARY = "summary"


	class MultiNews(datasets.GeneratorBasedBuilder):
	"""Multi-News dataset."""

	VERSION = datasets.Version("1.0.0")

	def _info(self):
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=datasets.Features({_DOCUMENT: datasets.Value("string"), _SUMMARY: datasets.Value("string")}),
	supervised_keys=(_DOCUMENT, _SUMMARY),
	homepage=_HOMEPAGE,
	license=_LICENSE,
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	"""Returns SplitGenerators."""
	files = dl_manager.download(_URLs)
	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	gen_kwargs={"src_file": files["train"][0], "tgt_file": files["train"][1]},
	),
	datasets.SplitGenerator(
	name=datasets.Split.VALIDATION,
	gen_kwargs={"src_file": files["val"][0], "tgt_file": files["val"][1]},
	),
	datasets.SplitGenerator(
	name=datasets.Split.TEST,
	gen_kwargs={"src_file": files["test"][0], "tgt_file": files["test"][1]},
	),
	]

	def _generate_examples(self, src_file, tgt_file):
	"""Yields examples."""
	with open(src_file, encoding="utf-8") as src_f, open(tgt_file, encoding="utf-8") as tgt_f:
	for i, (src_line, tgt_line) in enumerate(zip(src_f, tgt_f)):
	yield i, {
	# In original file, each line has one example and natural newline
	# tokens "\n" are being replaced with "NEWLINE_CHAR". Here restore
	# the natural newline token to avoid special vocab "NEWLINE_CHAR".
	_DOCUMENT: src_line.strip().replace("NEWLINE_CHAR", "\n"),
	_SUMMARY: tgt_line.strip(),
	}