Datasets:
Tasks:
Summarization
Languages:
English
Multilinguality:
monolingual
Size Categories:
100K<n<1M
Language Creators:
found
Annotations Creators:
found
Source Datasets:
original
ArXiv:
License:
File size: 5,348 Bytes
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# 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"]),
}
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