Upload mslr2022.py

mslr2022.py

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+# 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