Datasets:

abisee
/

cnn_dailymail

	# 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
	"""CNN/DailyMail Summarization dataset, non-anonymized version."""

	import hashlib
	import os

	import datasets


	logger = datasets.logging.get_logger(__name__)


	_HOMEPAGE = "https://github.com/abisee/cnn-dailymail"

	_DESCRIPTION = """\
	CNN/DailyMail non-anonymized summarization dataset.

	There are two features:
	- article: text of news article, used as the document to be summarized
	- highlights: joined text of highlights with <s> and </s> around each
	highlight, which is the target summary
	"""

	# The second citation introduces the source data, while the first
	# introduces the specific form (non-anonymized) we use here.
	_CITATION = """\
	@article{DBLP:journals/corr/SeeLM17,
	author = {Abigail See and
	Peter J. Liu and
	Christopher D. Manning},
	title = {Get To The Point: Summarization with Pointer-Generator Networks},
	journal = {CoRR},
	volume = {abs/1704.04368},
	year = {2017},
	url = {http://arxiv.org/abs/1704.04368},
	archivePrefix = {arXiv},
	eprint = {1704.04368},
	timestamp = {Mon, 13 Aug 2018 16:46:08 +0200},
	biburl = {https://dblp.org/rec/bib/journals/corr/SeeLM17},
	bibsource = {dblp computer science bibliography, https://dblp.org}
	}

	@inproceedings{hermann2015teaching,
	title={Teaching machines to read and comprehend},
	author={Hermann, Karl Moritz and Kocisky, Tomas and Grefenstette, Edward and Espeholt, Lasse and Kay, Will and Suleyman, Mustafa and Blunsom, Phil},
	booktitle={Advances in neural information processing systems},
	pages={1693--1701},
	year={2015}
	}
	"""

	_DL_URLS = {
	"cnn_stories": "https://huggingface.co/datasets/cnn_dailymail/resolve/11343c3752184397d56efc19a8a7cceb68089318/data/cnn_stories.tgz",
	"dm_stories": "https://huggingface.co/datasets/cnn_dailymail/resolve/11343c3752184397d56efc19a8a7cceb68089318/data/dailymail_stories.tgz",
	"train": "https://huggingface.co/datasets/cnn_dailymail/resolve/d20aeb41b7adb5b6800fbb08a6b1a3e9d9a90060/all_train.txt",
	"validation": "https://huggingface.co/datasets/cnn_dailymail/resolve/d20aeb41b7adb5b6800fbb08a6b1a3e9d9a90060/all_val.txt",
	"test": "https://huggingface.co/datasets/cnn_dailymail/resolve/d20aeb41b7adb5b6800fbb08a6b1a3e9d9a90060/all_test.txt",
	}

	_HIGHLIGHTS = "highlights"
	_ARTICLE = "article"

	_SUPPORTED_VERSIONS = [
	# Using local URL lists.
	datasets.Version("4.0.0", "Using HuggingFace Hosted URL Lists."),
	# Using cased version.
	datasets.Version("3.0.0", "Using cased version."),
	# Same data as 0.0.2
	datasets.Version("1.0.0", ""),
	# Having the model predict newline separators makes it easier to evaluate
	# using summary-level ROUGE.
	datasets.Version("2.0.0", "Separate target sentences with newline."),
	]


	_DEFAULT_VERSION = datasets.Version("4.0.0", "Using HuggingFace hosted URL lists.")


	class CnnDailymailConfig(datasets.BuilderConfig):
	"""BuilderConfig for CnnDailymail."""

	def __init__(self, **kwargs):
	"""BuilderConfig for CnnDailymail.

	Args:

	**kwargs: keyword arguments forwarded to super.
	"""
	super(CnnDailymailConfig, self).__init__(**kwargs)


	def _get_url_hashes(path):
	"""Get hashes of urls in file."""
	urls = _read_text_file_path(path)

	def url_hash(u):
	h = hashlib.sha1()
	try:
	u = u.encode("utf-8")
	except UnicodeDecodeError:
	logger.error("Cannot hash url: %s", u)
	h.update(u)
	return h.hexdigest()

	return {url_hash(u) for u in urls}


	def _get_hash_from_path(p):
	"""Extract hash from path."""
	return os.path.splitext(os.path.basename(p))[0]


	DM_SINGLE_CLOSE_QUOTE = "\u2019" # unicode
	DM_DOUBLE_CLOSE_QUOTE = "\u201d"
	# acceptable ways to end a sentence
	END_TOKENS = [".", "!", "?", "...", "'", "`", '"', DM_SINGLE_CLOSE_QUOTE, DM_DOUBLE_CLOSE_QUOTE, ")"]


	def _read_text_file_path(path):
	with open(path, "r", encoding="utf-8") as f:
	lines = [line.strip() for line in f]
	return lines


	def _read_text_file(file):
	return [line.decode("utf-8").strip() for line in file]


	def _get_art_abs(story_file, tfds_version):
	"""Get abstract (highlights) and article from a story file path."""
	# Based on https://github.com/abisee/cnn-dailymail/blob/master/
	# make_datafiles.py

	lines = _read_text_file(story_file)

	# The github code lowercase the text and we removed it in 3.0.0.

	# Put periods on the ends of lines that are missing them
	# (this is a problem in the dataset because many image captions don't end in
	# periods; consequently they end up in the body of the article as run-on
	# sentences)
	def fix_missing_period(line):
	"""Adds a period to a line that is missing a period."""
	if "@highlight" in line:
	return line
	if not line:
	return line
	if line[-1] in END_TOKENS:
	return line
	return line + " ."

	lines = [fix_missing_period(line) for line in lines]

	# Separate out article and abstract sentences
	article_lines = []
	highlights = []
	next_is_highlight = False
	for line in lines:
	if not line:
	continue # empty line
	elif line.startswith("@highlight"):
	next_is_highlight = True
	elif next_is_highlight:
	highlights.append(line)
	else:
	article_lines.append(line)

	# Make article into a single string
	article = " ".join(article_lines)

	if tfds_version >= "2.0.0":
	abstract = "\n".join(highlights)
	else:
	abstract = " ".join(highlights)

	return article, abstract


	class CnnDailymail(datasets.GeneratorBasedBuilder):
	"""CNN/DailyMail non-anonymized summarization dataset."""

	BUILDER_CONFIGS = [
	CnnDailymailConfig(name=str(version), description="Plain text", version=version)
	for version in _SUPPORTED_VERSIONS
	]

	def _info(self):
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=datasets.Features(
	{
	_ARTICLE: datasets.Value("string"),
	_HIGHLIGHTS: datasets.Value("string"),
	"id": datasets.Value("string"),
	}
	),
	supervised_keys=None,
	homepage=_HOMEPAGE,
	citation=_CITATION,
	)

	def _vocab_text_gen(self, paths):
	for _, ex in self._generate_examples(paths):
	yield " ".join([ex[_ARTICLE], ex[_HIGHLIGHTS]])

	def _split_generators(self, dl_manager):
	dl_paths = dl_manager.download(_DL_URLS)
	return [
	datasets.SplitGenerator(
	name=split,
	gen_kwargs={
	"urls_file": dl_paths[split],
	"files_per_archive": [
	dl_manager.iter_archive(dl_paths["cnn_stories"]),
	dl_manager.iter_archive(dl_paths["dm_stories"]),
	],
	},
	)
	for split in [datasets.Split.TRAIN, datasets.Split.VALIDATION, datasets.Split.TEST]
	]

	def _generate_examples(self, urls_file, files_per_archive):
	urls = _get_url_hashes(urls_file)
	idx = 0
	for files in files_per_archive:
	for path, file in files:
	hash_from_path = _get_hash_from_path(path)
	if hash_from_path in urls:
	article, highlights = _get_art_abs(file, self.config.version)
	if not article or not highlights:
	continue
	yield idx, {
	_ARTICLE: article,
	_HIGHLIGHTS: highlights,
	"id": hash_from_path,
	}
	idx += 1