Datasets:
Languages:
English
Multilinguality:
monolingual
Size Categories:
100K<n<1M
Language Creators:
found
Annotations Creators:
no-annotation
Source Datasets:
original
License:
| # 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__) | |
| _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 = { | |
| # pylint: disable=line-too-long | |
| "cnn_stories": "cnn_stories.tgz", | |
| "dm_stories": "dailymail_stories.tgz", | |
| "test_urls": "https://raw.githubusercontent.com/abisee/cnn-dailymail/master/url_lists/all_test.txt", | |
| "train_urls": "https://raw.githubusercontent.com/abisee/cnn-dailymail/master/url_lists/all_train.txt", | |
| "val_urls": "https://raw.githubusercontent.com/abisee/cnn-dailymail/master/url_lists/all_val.txt", | |
| # pylint: enable=line-too-long | |
| } | |
| _HIGHLIGHTS = "highlights" | |
| _ARTICLE = "article" | |
| _SUPPORTED_VERSIONS = [ | |
| # 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("3.0.0", "Using cased version.") | |
| DEFAULT_CONFIG_NAME = "3.0.0" | |
| 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) | |
| 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): True for u in urls} | |
| def _get_hash_from_path(p): | |
| """Extract hash from path.""" | |
| basename = os.path.basename(p) | |
| return basename[0 : basename.find(".story")] | |
| def _find_files(dl_paths, publisher, url_dict): | |
| """Find files corresponding to urls.""" | |
| if publisher == "cnn": | |
| top_dir = os.path.join(dl_paths["cnn_stories"], "cnn", "stories") | |
| elif publisher == "dm": | |
| top_dir = os.path.join(dl_paths["dm_stories"], "dailymail", "stories") | |
| else: | |
| logger.fatal("Unsupported publisher: %s", publisher) | |
| files = sorted(os.listdir(top_dir)) | |
| ret_files = [] | |
| for p in files: | |
| if _get_hash_from_path(p) in url_dict: | |
| ret_files.append(os.path.join(top_dir, p)) | |
| return ret_files | |
| def _subset_filenames(dl_paths, split): | |
| """Get filenames for a particular split.""" | |
| assert isinstance(dl_paths, dict), dl_paths | |
| # Get filenames for a split. | |
| if split == datasets.Split.TRAIN: | |
| urls = _get_url_hashes(dl_paths["train_urls"]) | |
| elif split == datasets.Split.VALIDATION: | |
| urls = _get_url_hashes(dl_paths["val_urls"]) | |
| elif split == datasets.Split.TEST: | |
| urls = _get_url_hashes(dl_paths["test_urls"]) | |
| else: | |
| logger.fatal("Unsupported split: %s", split) | |
| cnn = _find_files(dl_paths, "cnn", urls) | |
| dm = _find_files(dl_paths, "dm", urls) | |
| return cnn + dm | |
| 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(text_file): | |
| lines = [] | |
| with open(text_file, "r", encoding="utf-8") as f: | |
| for line in f: | |
| lines.append(line.strip()) | |
| return lines | |
| 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): | |
| # Should return a datasets.DatasetInfo object | |
| return datasets.DatasetInfo( | |
| description=_DESCRIPTION, | |
| features=datasets.Features( | |
| { | |
| _ARTICLE: datasets.Value("string"), | |
| _HIGHLIGHTS: datasets.Value("string"), | |
| "id": datasets.Value("string"), | |
| } | |
| ), | |
| supervised_keys=None, | |
| homepage="https://github.com/abisee/cnn-dailymail", | |
| 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_and_extract(_DL_URLS) | |
| train_files = _subset_filenames(dl_paths, datasets.Split.TRAIN) | |
| # Generate shared vocabulary | |
| return [ | |
| datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"files": train_files}), | |
| datasets.SplitGenerator( | |
| name=datasets.Split.VALIDATION, | |
| gen_kwargs={"files": _subset_filenames(dl_paths, datasets.Split.VALIDATION)}, | |
| ), | |
| datasets.SplitGenerator( | |
| name=datasets.Split.TEST, gen_kwargs={"files": _subset_filenames(dl_paths, datasets.Split.TEST)} | |
| ), | |
| ] | |
| def _generate_examples(self, files): | |
| for p in files: | |
| article, highlights = _get_art_abs(p, self.config.version) | |
| if not article or not highlights: | |
| continue | |
| fname = os.path.basename(p) | |
| yield fname, { | |
| _ARTICLE: article, | |
| _HIGHLIGHTS: highlights, | |
| "id": _get_hash_from_path(fname), | |
| } | |