Datasets:
polsum

	# coding=utf-8
	# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
	#
	# 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.
	"""Polish Summaries Corpus: the corpus of Polish news summaries"""


	import glob
	import xml.etree.ElementTree as ET

	import datasets


	# TODO: Add BibTeX citation
	# Find for instance the citation on arxiv or on the dataset repo/website
	_CITATION = """\
	@inproceedings{
	ogro:kop:14:lrec,
	author = "Ogrodniczuk, Maciej and Kopeć, Mateusz",
	pdf = "http://nlp.ipipan.waw.pl/Bib/ogro:kop:14:lrec.pdf",
	title = "The {P}olish {S}ummaries {C}orpus",
	pages = "3712--3715",
	crossref = "lrec:14"
	}
	@proceedings{
	lrec:14,
	editor = "Calzolari, Nicoletta and Choukri, Khalid and Declerck, Thierry and Loftsson, Hrafn and Maegaard, Bente and Mariani, Joseph and Moreno, Asuncion and Odijk, Jan and Piperidis, Stelios",
	isbn = "978-2-9517408-8-4",
	title = "Proceedings of the Ninth International {C}onference on {L}anguage {R}esources and {E}valuation, {LREC}~2014",
	url = "http://www.lrec-conf.org/proceedings/lrec2014/index.html",
	booktitle = "Proceedings of the Ninth International {C}onference on {L}anguage {R}esources and {E}valuation, {LREC}~2014",
	address = "Reykjavík, Iceland",
	key = "LREC",
	year = "2014",
	organization = "European Language Resources Association (ELRA)"
	}
	"""

	# TODO: Add description of the dataset here
	# You can copy an official description
	_DESCRIPTION = """\
	Polish Summaries Corpus: the corpus of Polish news summaries.
	"""

	# TODO: Add a link to an official homepage for the dataset here
	_HOMEPAGE = "http://zil.ipipan.waw.pl/PolishSummariesCorpus"

	# TODO: Add the licence for the dataset here if you can find it
	_LICENSE = "CC BY v.3"

	# TODO: Add link to the official dataset URLs here
	# The HuggingFace dataset library don't host the datasets but only point to the original files
	# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
	_URL = "http://zil.ipipan.waw.pl/PolishSummariesCorpus?action=AttachFile&do=get&target=PSC_1.0.zip"


	# TODO: Name of the dataset usually match the script name with CamelCase instead of snake_case
	class Polsum(datasets.GeneratorBasedBuilder):
	"""Polish Summaries Corpus: the corpus of Polish news summaries."""

	VERSION = datasets.Version("1.1.0")

	def _info(self):
	features = datasets.Features(
	{
	"id": datasets.Value("string"),
	"date": datasets.Value("string"),
	"title": datasets.Value("string"),
	"section": datasets.Value("string"),
	"authors": datasets.Value("string"),
	"body": datasets.Value("string"),
	"summaries": datasets.features.Sequence(
	{
	"ratio": datasets.Value("int32"),
	"type": datasets.Value("string"),
	"author": datasets.Value("string"),
	"body": datasets.Value("string"),
	"spans": datasets.features.Sequence(
	{
	"start": datasets.Value("int32"),
	"end": datasets.Value("int32"),
	"span_text": datasets.Value("string"),
	}
	),
	}
	),
	}
	)
	return datasets.DatasetInfo(
	# This is the description that will appear on the datasets page.
	description=_DESCRIPTION,
	# This defines the different columns of the dataset and their types
	features=features, # Here we define them above because they are different between the two configurations
	# If there's a common (input, target) tuple from the features,
	# specify them here. They'll be used if as_supervised=True in
	# builder.as_dataset.
	supervised_keys=None,
	# Homepage of the dataset for documentation
	homepage=_HOMEPAGE,
	# License for the dataset if available
	license=_LICENSE,
	# Citation for the dataset
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	"""Returns SplitGenerators."""
	# dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLs
	# It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
	# By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
	data_dir = dl_manager.download_and_extract(_URL)
	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={
	"filepaths": glob.glob(data_dir + "///*.xml"),
	},
	),
	]

	def _generate_examples(self, filepaths):
	"""Yields examples."""
	# TODO: This method will receive as arguments the `gen_kwargs` defined in the previous `_split_generators` method.
	# It is in charge of opening the given file and yielding (key, example) tuples from the dataset
	# The key is not important, it's more here for legacy reason (legacy from tfds)

	for i, xml_path in enumerate(sorted(filepaths)):
	root = ET.parse(xml_path).getroot()
	text_id = root.get("id")
	date_tag = root.find("date")
	date = date_tag.text.strip()
	title_tag = root.find("title")
	title = title_tag.text.strip()
	section_tag = root.find("section")
	section = section_tag.text.strip()
	authors_tag = root.find("authors")
	authors = authors_tag.text.strip()
	body_tag = root.find("body")
	body = body_tag.text.strip()
	summaries_tag = root.find("summaries")
	summaries = []
	for summary_tag in summaries_tag.iterfind("summary"):
	sratio = int(summary_tag.get("ratio"))
	stype = summary_tag.get("type")
	sauthor = summary_tag.get("author")
	sbody_tag = summary_tag.find("body")
	sbody = sbody_tag.text.strip()
	spans_tag = summary_tag.find("spans")
	spans = []
	if spans_tag:
	for span_tag in spans_tag.iterfind("span"):
	start = int(span_tag.get("start"))
	end = int(span_tag.get("end"))
	span_text = span_tag.text.strip()
	spans.append(
	{
	"start": start,
	"end": end,
	"span_text": span_text,
	}
	)
	summaries.append(
	{
	"ratio": sratio,
	"type": stype,
	"author": sauthor,
	"body": sbody,
	"spans": spans,
	}
	)

	yield i, {
	"id": text_id,
	"date": date,
	"title": title,
	"section": section,
	"authors": authors,
	"body": body,
	"summaries": summaries,
	}