Datasets:
gigaword

	# coding=utf-8
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	#
	# Licensed under the Apache License, Version 2.0 (the "License");
	# you may not use this file except in compliance with the License.
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	#
	# http://www.apache.org/licenses/LICENSE-2.0
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	# Lint as: python3
	"""Gigaword summarization dataset."""


	import os

	import datasets


	_CITATION = """
	@article{graff2003english,
	title={English gigaword},
	author={Graff, David and Kong, Junbo and Chen, Ke and Maeda, Kazuaki},
	journal={Linguistic Data Consortium, Philadelphia},
	volume={4},
	number={1},
	pages={34},
	year={2003}
	}

	@article{Rush_2015,
	title={A Neural Attention Model for Abstractive Sentence Summarization},
	url={http://dx.doi.org/10.18653/v1/D15-1044},
	DOI={10.18653/v1/d15-1044},
	journal={Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing},
	publisher={Association for Computational Linguistics},
	author={Rush, Alexander M. and Chopra, Sumit and Weston, Jason},
	year={2015}
	}
	"""

	_DESCRIPTION = """
	Headline-generation on a corpus of article pairs from Gigaword consisting of
	around 4 million articles. Use the 'org_data' provided by
	https://github.com/microsoft/unilm/ which is identical to
	https://github.com/harvardnlp/sent-summary but with better format.

	There are two features:
	- document: article.
	- summary: headline.

	"""

	# Source: https://drive.google.com/uc?export=download&id=1USoQ8lJgN8kAWnUnRrupMGrPMLlDVqlV
	_URL = "data/ggw_data.zip"

	_DOCUMENT = "document"
	_SUMMARY = "summary"


	class Gigaword(datasets.GeneratorBasedBuilder):
	"""Gigaword summarization dataset."""

	# 1.0.0 contains a bug that uses validation data as training data.
	# 1.1.0 Update to the correct train, validation and test data.
	# 1.2.0 Replace <unk> with <UNK> in train/val to be consistent with test.
	VERSION = datasets.Version("1.2.0")

	def _info(self):
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=datasets.Features({_DOCUMENT: datasets.Value("string"), _SUMMARY: datasets.Value("string")}),
	supervised_keys=(_DOCUMENT, _SUMMARY),
	homepage="https://github.com/harvardnlp/sent-summary",
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	"""Returns SplitGenerators."""
	dl_path = dl_manager.download_and_extract(_URL)
	pattern = os.path.join(dl_path, "org_data", "%s.%s.txt")
	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	gen_kwargs={
	"src_path": pattern % ("train", "src"),
	"tgt_path": pattern % ("train", "tgt"),
	"replace_unk": True,
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split.VALIDATION,
	gen_kwargs={
	"src_path": pattern % ("dev", "src"),
	"tgt_path": pattern % ("dev", "tgt"),
	"replace_unk": True,
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split.TEST,
	gen_kwargs={
	"src_path": pattern % ("test", "src"),
	"tgt_path": pattern % ("test", "tgt"),
	"replace_unk": False,
	},
	),
	]

	def _generate_examples(self, src_path=None, tgt_path=None, replace_unk=None):
	"""Yields examples."""
	with open(src_path, encoding="utf-8") as f_d, open(tgt_path, encoding="utf-8") as f_s:
	for i, (doc_text, sum_text) in enumerate(zip(f_d, f_s)):
	if replace_unk:
	yield i, {
	_DOCUMENT: doc_text.strip().replace("<unk>", "UNK"),
	_SUMMARY: sum_text.strip().replace("<unk>", "UNK"),
	}
	else:
	yield i, {_DOCUMENT: doc_text.strip(), _SUMMARY: sum_text.strip()}