Datasets:
Tasks:
Summarization
Languages:
English
Multilinguality:
monolingual
Size Categories:
100K<n<1M
Language Creators:
found
Annotations Creators:
found
Source Datasets:
original
ArXiv:
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 | |
"""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"]), | |
} | |