Datasets:
newsroom

	# 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
	#
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	# 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
	"""NEWSROOM Dataset."""


	import json
	import os

	import datasets


	_CITATION = """
	@inproceedings{N18-1065,
	author = {Grusky, Max and Naaman, Mor and Artzi, Yoav},
	title = {NEWSROOM: A Dataset of 1.3 Million Summaries
	with Diverse Extractive Strategies},
	booktitle = {Proceedings of the 2018 Conference of the
	North American Chapter of the Association for
	Computational Linguistics: Human Language Technologies},
	year = {2018},
	}
	"""

	_DESCRIPTION = """
	NEWSROOM is a large dataset for training and evaluating summarization systems.
	It contains 1.3 million articles and summaries written by authors and
	editors in the newsrooms of 38 major publications.

	Dataset features includes:
	- text: Input news text.
	- summary: Summary for the news.
	And additional features:
	- title: news title.
	- url: url of the news.
	- date: date of the article.
	- density: extractive density.
	- coverage: extractive coverage.
	- compression: compression ratio.
	- density_bin: low, medium, high.
	- coverage_bin: extractive, abstractive.
	- compression_bin: low, medium, high.

	This dataset can be downloaded upon requests. Unzip all the contents
	"train.jsonl, dev.josnl, test.jsonl" to the tfds folder.

	"""

	_DOCUMENT = "text"
	_SUMMARY = "summary"
	_ADDITIONAL_TEXT_FEATURES = [
	"title",
	"url",
	"date",
	"density_bin",
	"coverage_bin",
	"compression_bin",
	]
	_ADDITIONAL_FLOAT_FEATURES = [
	"density",
	"coverage",
	"compression",
	]


	class Newsroom(datasets.GeneratorBasedBuilder):
	"""NEWSROOM Dataset."""

	VERSION = datasets.Version("1.0.0")

	@property
	def manual_download_instructions(self):
	return """\
	You should download the dataset from https://lil.nlp.cornell.edu/newsroom/download/index.html
	The webpage requires registration.
	To unzip the .tar file run `tar -zxvf complete.tar`. To unzip the .gz files
	run `gunzip train.json.gz` , ...
	After downloading, please put the files under the following names
	dev.jsonl, test.jsonl and train.jsonl in a dir of your choice,
	which will be used as a manual_dir, e.g. `~/.manual_dirs/newsroom`
	Newsroom can then be loaded via:
	`datasets.load_dataset("newsroom", data_dir="~/.manual_dirs/newsroom")`.
	"""

	def _info(self):
	features = {k: datasets.Value("string") for k in [_DOCUMENT, _SUMMARY] + _ADDITIONAL_TEXT_FEATURES}
	features.update({k: datasets.Value("float32") for k in _ADDITIONAL_FLOAT_FEATURES})
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=datasets.Features(features),
	supervised_keys=(_DOCUMENT, _SUMMARY),
	homepage="https://lil.nlp.cornell.edu/newsroom/index.html",
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	"""Returns SplitGenerators."""
	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('newsroom', data_dir=...)` that includes files unzipped from the reclor zip. Manual download instructions: {self.manual_download_instructions}"
	)
	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	gen_kwargs={"input_file": os.path.join(data_dir, "train.jsonl")},
	),
	datasets.SplitGenerator(
	name=datasets.Split.VALIDATION,
	gen_kwargs={"input_file": os.path.join(data_dir, "dev.jsonl")},
	),
	datasets.SplitGenerator(
	name=datasets.Split.TEST,
	gen_kwargs={"input_file": os.path.join(data_dir, "test.jsonl")},
	),
	]

	def _generate_examples(self, input_file=None):
	"""Yields examples."""
	with open(input_file, encoding="utf-8") as f:
	for i, line in enumerate(f):
	d = json.loads(line)
	# fields are "url", "archive", "title", "date", "text",
	# "compression_bin", "density_bin", "summary", "density",
	# "compression', "coverage", "coverage_bin",
	yield i, {
	k: d[k] for k in [_DOCUMENT, _SUMMARY] + _ADDITIONAL_TEXT_FEATURES + _ADDITIONAL_FLOAT_FEATURES
	}