Datasets:

tomasg25
/

scientific_lay_summarisation

	# 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 Lay Summarization Datasets."""


	import json
	import os

	import datasets


	_CITATION = """
	@misc{Goldsack_2022,
	doi = {10.48550/ARXIV.2210.09932},
	url = {https://arxiv.org/abs/2210.09932},
	author = {Goldsack, Tomas and Zhang, Zhihao and Lin, Chenghua and Scarton, Carolina},
	title = {Making Science Simple: Corpora for the Lay Summarisation of Scientific Literature},
	publisher = {arXiv},
	year = {2022},
	copyright = {arXiv.org perpetual, non-exclusive license}
	}
	"""

	_DESCRIPTION = """
	This repository contains the PLOS and eLife datasets, introduced in the EMNLP 2022 paper "[Making Science Simple: Corpora for the Lay Summarisation of Scientific Literature
	](https://arxiv.org/abs/2210.09932)".
	Each dataset contains full biomedical research articles paired with expert-written lay summaries (i.e., non-technical summaries). PLOS articles are derived from various journals published by [the Public Library of Science (PLOS)](https://plos.org/), whereas eLife articles are derived from the [eLife](https://elifesciences.org/) journal. More details/anlaysis on the content of each dataset are provided in the paper.

	Both "elife" and "plos" have 6 features:
	- "article": the body of the document (including the abstract), sections seperated by "/n".
	- "section_headings": the title of each section, seperated by "/n".
	- "keywords": keywords describing the topic of the article, seperated by "/n".
	- "title" : the title of the article.
	- "year" : the year the article was published.
	- "summary": the lay summary of the document.
	"""

	_DOCUMENT = "article"
	_SUMMARY = "summary"

	_URLS = {
	"plos": "https://drive.usercontent.google.com/download?id=1lZ6PCAtXvmGjRZyp3vQQCEgO_yerH62Q&export=download&authuser=1&confirm=t&uuid=dc63dea1-0814-450f-9234-8bff2b9d1a94&at=APZUnTUfgwJ5Tdiin4ppFPPLWhMX%3A1716450460802",
	"elife": "https://drive.usercontent.google.com/download?id=1WKW8BAqluOlXrpy1B9mV3j3CtAK3JdnE&export=download&authuser=1&confirm=t&uuid=1332bc11-7cbf-4c4d-8561-85621060f397&at=APZUnTVLLKAGVSBpQlYKojrJ57xb%3A1716450570186",
	}


	class ScientificLaySummarisationConfig(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.
	"""
	super(ScientificLaySummarisationConfig, self).__init__(version=datasets.Version("1.0.0"), **kwargs)
	self.filename = filename


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

	BUILDER_CONFIGS = [
	ScientificLaySummarisationConfig(name="plos", description="Documents and lay summaries from PLOS journals."),
	ScientificLaySummarisationConfig(name="elife", description="Documents and lay summaries from the eLife journal."),
	]

	def _info(self):
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=datasets.Features(
	{
	_DOCUMENT: datasets.Value("string"),
	_SUMMARY: datasets.Value("string"),
	"section_headings": datasets.Value("string"),
	"keywords": datasets.Value("string"),
	"year": datasets.Value("string"),
	"title": datasets.Value("string"),
	}
	),
	supervised_keys=None,
	homepage="https://github.com/TGoldsack1/Corpora_for_Lay_Summarisation",
	citation=_CITATION,
	)

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

	def _generate_examples(self, path=None):
	"""Yields examples."""
	with open(path, encoding="utf-8") as f:
	f = json.loads(f.read())
	for d in f:
	# Possible keys are:
	# "id": str, # unique identifier
	# "year": int, # year of publication
	# "title": str, # title
	# "sections": List[List[str]], # main text, divided in to sections/sentences
	# "headings" List[str], # headings of each section
	# "abstract": List[str], # abstract, in sentences
	# "summary": List[str], # lay summary, in sentences
	# "keywords": List[str] # keywords/topic of article

	sections = [" ".join(s).strip() for s in d["sections"]]
	abstract = " ".join(d['abstract']).strip()
	full_doc = [abstract] + sections
	summary = " ".join(d["summary"]).strip()

	yield d["id"], {
	_DOCUMENT: "\n".join(full_doc),
	_SUMMARY: summary,
	"section_headings": "\n".join(["Abstract"] + d["headings"]),
	"keywords": "\n".join(d["keywords"]),
	"year": d["year"],
	"title": d["title"]
	}