cnn_dailymail / cnn_dailymail.py
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# 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