Datasets:
scientific_papers

	# 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.
	# 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
	"""Scientific Papers Dataset."""


	import json
	import os

	import datasets


	_CITATION = """
	@article{Cohan_2018,
	title={A Discourse-Aware Attention Model for Abstractive Summarization of
	Long Documents},
	url={http://dx.doi.org/10.18653/v1/n18-2097},
	DOI={10.18653/v1/n18-2097},
	journal={Proceedings of the 2018 Conference of the North American Chapter of
	the Association for Computational Linguistics: Human Language
	Technologies, Volume 2 (Short Papers)},
	publisher={Association for Computational Linguistics},
	author={Cohan, Arman and Dernoncourt, Franck and Kim, Doo Soon and Bui, Trung and Kim, Seokhwan and Chang, Walter and Goharian, Nazli},
	year={2018}
	}
	"""

	_DESCRIPTION = """
	Scientific papers datasets contains two sets of long and structured documents.
	The datasets are obtained from ArXiv and PubMed OpenAccess repositories.

	Both "arxiv" and "pubmed" have two features:
	- article: the body of the document, pagragraphs seperated by "/n".
	- abstract: the abstract of the document, pagragraphs seperated by "/n".
	- section_names: titles of sections, seperated by "/n".

	"""

	_DOCUMENT = "article"
	_SUMMARY = "abstract"

	_URLS = {
	"arxiv": "https://s3.amazonaws.com/datasets.huggingface.co/scientific_papers/1.1.1/arxiv-dataset.zip",
	"pubmed": "https://s3.amazonaws.com/datasets.huggingface.co/scientific_papers/1.1.1/pubmed-dataset.zip",
	}


	class ScientificPapersConfig(datasets.BuilderConfig):
	"""BuilderConfig for Scientific Papers."""

	def __init__(self, filename=None, **kwargs):
	"""BuilderConfig for ScientificPapers

	Args:
	filename: filename of different configs for the dataset.
	**kwargs: keyword arguments forwarded to super.
	"""
	# 1.1.0 remove sentence breaker <S> and </S> in summary.
	super(ScientificPapersConfig, self).__init__(version=datasets.Version("1.1.1"), **kwargs)
	self.filename = filename


	class ScientificPapers(datasets.GeneratorBasedBuilder):
	"""Scientific Papers."""

	BUILDER_CONFIGS = [
	ScientificPapersConfig(name="pubmed", description="Documents from PubMed repository."),
	ScientificPapersConfig(name="arxiv", description="Documents from ArXiv repository."),
	]

	def _info(self):
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=datasets.Features(
	{
	_DOCUMENT: datasets.Value("string"),
	_SUMMARY: datasets.Value("string"),
	"section_names": datasets.Value("string"),
	}
	),
	supervised_keys=None,
	homepage="https://github.com/armancohan/long-summarization",
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	"""Returns SplitGenerators."""
	dl_paths = dl_manager.download_and_extract(_URLS)
	path = os.path.join(dl_paths[self.config.name], self.config.name + "-dataset")
	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	gen_kwargs={"path": os.path.join(path, "train.txt")},
	),
	datasets.SplitGenerator(
	name=datasets.Split.VALIDATION,
	gen_kwargs={"path": os.path.join(path, "val.txt")},
	),
	datasets.SplitGenerator(
	name=datasets.Split.TEST,
	gen_kwargs={"path": os.path.join(path, "test.txt")},
	),
	]

	def _generate_examples(self, path=None):
	"""Yields examples."""
	with open(path, encoding="utf-8") as f:
	for line in f:
	# Possible keys are:
	# "article_id": str
	# "article_text": list[str] article (list of paragraphs).
	# "abstract_text": list[str], abstract (list of paragraphs).
	# "section_names": list[str], list of section names.
	# "sections": list[list[str]], list of sections (list of paragraphs)
	d = json.loads(line)
	summary = "\n".join(d["abstract_text"])
	# In original paper, <S> and </S> are not used in vocab during training
	# or during decoding.
	# https://github.com/armancohan/long-summarization/blob/master/data.py#L27
	summary = summary.replace("<S>", "").replace("</S>", "")
	yield d["article_id"], {
	_DOCUMENT: "\n".join(d["article_text"]),
	_SUMMARY: summary,
	"section_names": "\n".join(d["section_names"]),
	}