polsum / polsum.py
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# 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,
# 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 = """\
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"
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
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.
# 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.
# Homepage of the dataset for documentation
# License for the dataset if available
# Citation for the dataset
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 [
# These kwargs will be passed to _generate_examples
"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()
"start": start,
"end": end,
"span_text": span_text,
"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,