Datasets:
Languages:
English
Multilinguality:
monolingual
Size Categories:
10K<n<100K
Language Creators:
expert-generated
Annotations Creators:
expert-generated
Tags:
License:
# 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. | |
""" | |
The Multidocument Summarization for Literature Review (MSLR) Shared Task aims to study how medical | |
evidence from different clinical studies are summarized in literature reviews. Reviews provide the | |
highest quality of evidence for clinical care, but are expensive to produce manually. | |
(Semi-)automation via NLP may facilitate faster evidence synthesis without sacrificing rigor. The | |
MSLR shared task uses two datasets to assess the current state of multidocument summarization for | |
this task, and to encourage the development of modeling contributions, scaffolding tasks, methods | |
for model interpretability, and improved automated evaluation methods in this domain. | |
""" | |
import os | |
import pandas as pd | |
import datasets | |
_CITATION = """\ | |
@inproceedings{DeYoung2021MS2MS, | |
title = {MSˆ2: Multi-Document Summarization of Medical Studies}, | |
author = {Jay DeYoung and Iz Beltagy and Madeleine van Zuylen and Bailey Kuehl and Lucy Lu Wang}, | |
booktitle = {EMNLP}, | |
year = {2021} | |
} | |
@article{Wallace2020GeneratingN, | |
title = {Generating (Factual?) Narrative Summaries of RCTs: Experiments with Neural Multi-Document Summarization}, | |
author = {Byron C. Wallace and Sayantani Saha and Frank Soboczenski and Iain James Marshall}, | |
year = 2020, | |
journal = {AMIA Annual Symposium}, | |
volume = {abs/2008.11293} | |
} | |
""" | |
_DATASETNAME = "mslr2022" | |
_DESCRIPTION = """\ | |
The Multidocument Summarization for Literature Review (MSLR) Shared Task aims to study how medical | |
evidence from different clinical studies are summarized in literature reviews. Reviews provide the | |
highest quality of evidence for clinical care, but are expensive to produce manually. | |
(Semi-)automation via NLP may facilitate faster evidence synthesis without sacrificing rigor. | |
The MSLR shared task uses two datasets to assess the current state of multidocument summarization | |
for this task, and to encourage the development of modeling contributions, scaffolding tasks, methods | |
for model interpretability, and improved automated evaluation methods in this domain. | |
""" | |
_HOMEPAGE = "https://github.com/allenai/mslr-shared-task" | |
_LICENSE = "Apache-2.0" | |
_URLS = { | |
_DATASETNAME: "https://ai2-s2-mslr.s3.us-west-2.amazonaws.com/mslr_data.tar.gz", | |
} | |
class MSLR2022(datasets.GeneratorBasedBuilder): | |
"""MSLR2022 Shared Task.""" | |
VERSION = datasets.Version("1.0.0") | |
BUILDER_CONFIGS = [ | |
datasets.BuilderConfig( | |
name="ms2", | |
version=VERSION, | |
description="This dataset consists of around 20K reviews and 470K studies collected from PubMed. For details on dataset contents and construction, please read the MS^2 paper (https://arxiv.org/abs/2104.06486).", | |
), | |
datasets.BuilderConfig( | |
name="cochrane", | |
version=VERSION, | |
description="This is a dataset of 4.5K reviews collected from Cochrane systematic reviews. For details on dataset contents and construction, please read the AMIA paper (https://arxiv.org/abs/2008.11293).", | |
), | |
] | |
def _info(self): | |
fields = { | |
"review_id": datasets.Value("string"), | |
"pmid": datasets.Sequence(datasets.Value("string")), | |
"title": datasets.Sequence(datasets.Value("string")), | |
"abstract": datasets.Sequence(datasets.Value("string")), | |
"target": datasets.Value("string"), | |
} | |
# These are unique to MS^2 | |
if self.config.name == "ms2": | |
fields["background"] = datasets.Value("string") | |
features = datasets.Features(fields) | |
return datasets.DatasetInfo( | |
description=_DESCRIPTION, | |
features=features, | |
homepage=_HOMEPAGE, | |
license=_LICENSE, | |
citation=_CITATION, | |
) | |
def _split_generators(self, dl_manager): | |
urls = _URLS[_DATASETNAME] | |
data_dir = dl_manager.download_and_extract(urls) | |
mslr_data_dir = os.path.join(data_dir, "mslr_data", self.config.name) | |
return [ | |
datasets.SplitGenerator( | |
name=datasets.Split.TRAIN, | |
gen_kwargs={ | |
"data_dir": mslr_data_dir, | |
"split": "train", | |
}, | |
), | |
datasets.SplitGenerator( | |
name=datasets.Split.TEST, | |
gen_kwargs={"data_dir": mslr_data_dir, "split": "test"}, | |
), | |
datasets.SplitGenerator( | |
name=datasets.Split.VALIDATION, | |
gen_kwargs={ | |
"data_dir": mslr_data_dir, | |
"split": "dev", | |
}, | |
), | |
] | |
def _generate_examples(self, data_dir, split): | |
inputs_filepath = os.path.join(data_dir, f"{split}-inputs.csv") | |
# At least one element in ReviewID is not a string, so explicitly cast it as such | |
inputs_df = pd.read_csv(inputs_filepath, index_col=0, dtype={"ReviewID": "string"}) | |
# Only the train and dev splits have targets | |
if split != "test": | |
targets_filepath = os.path.join(data_dir, f"{split}-targets.csv") | |
targets_df = pd.read_csv(targets_filepath, index_col=0, dtype={"ReviewID": "string"}) | |
# Only MS^2 has the *-reviews-info.csv files | |
if self.config.name == "ms2": | |
reviews_info_filepath = os.path.join(data_dir, f"{split}-reviews-info.csv") | |
reviews_info_df = pd.read_csv(reviews_info_filepath, index_col=0, dtype={"ReviewID": "string"}) | |
for review_id in inputs_df.ReviewID.unique(): | |
inputs = inputs_df[inputs_df.ReviewID == review_id] | |
example = { | |
"review_id": review_id, | |
"pmid": inputs.PMID.values.tolist(), | |
"title": inputs.Title.values.tolist(), | |
"abstract": inputs.Abstract.values.tolist(), | |
"target": "", | |
} | |
# Only the train and dev splits have targets | |
if split != "test": | |
targets = targets_df[targets_df.ReviewID == review_id] | |
example["target"] = targets.Target.values[0] | |
# Only MS^2 has the background section | |
if self.config.name == "ms2": | |
reviews_info = reviews_info_df[reviews_info_df.ReviewID == review_id] | |
example["background"] = reviews_info.Background.values[0] | |
yield review_id, example | |