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med_paragraph_simplification.py

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+# coding=utf-8
+# 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.
+"""Covid Dialog dataset in English and Chinese"""
+
+
+import copy
+import os
+import re
+import textwrap
+
+import datasets
+
+
+# BibTeX citation
+_CITATION = """\
+    @inproceedings{devaraj-etal-2021-paragraph,
+        title = "Paragraph-level Simplification of Medical Texts",
+        author = "Devaraj, Ashwin and Marshall, Iain and Wallace, Byron and Li, Junyi Jessy",
+        booktitle = "Proceedings of the 2021 Conference of the North American Chapter of the Association for
+                     Computational Linguistics",
+        month = jun,
+        year = "2021",
+        publisher = "Association for Computational Linguistics",
+        url = "https://www.aclweb.org/anthology/2021.naacl-main.395",
+        pages = "4972--4984",
+
+"""
+
+# Official description of the dataset
+_DESCRIPTION = textwrap.dedent(
+    """
+    "Paragraph-level Simplification of Medical Texts" (Devaraj et al.) studies the problem of learning to simplify
+    medical texts. One of their contributions is a new corpus that is composed of technical abstracts and their
+    lay summaries on various clinical topics.
+
+    The author generated train/val/test splits, which are available in the GitHub repository linked in the paper.
+
+    The following is an example from the dataset:
+
+    {
+        "doi": "10.1002/14651858.CD011112.pub2",
+        "abstract": "We included six studies (reported as seven papers) involving 326 participants whose ages ranged
+        from 39 to 83 years, with a gender bias towards men (73% to 95% across studies), reflecting the characteristics
+        of patients with HNC. The risk of bias in the studies was generally high. We did not pool data from studies
+        because of significant differences in the interventions and outcomes evaluated. We found a lack of
+        standardisation and consistency in the outcomes measured and the endpoints at which they were evaluated.
+        We found no evidence that therapeutic exercises were better than TAU, or any other treatment, in improving the
+        safety and efficiency of oral swallowing (our primary outcome) or in improving any of the secondary outcomes.
+        Using the GRADE system, we classified the overall quality of the evidence for each outcome as very low, due to
+        the limited number of trials and their low quality. There were no adverse events reported that were directly
+        attributable to the intervention (swallowing exercises). We found no evidence that undertaking therapeutic
+        exercises before, during and/or immediately after HNC treatment leads to improvement in oral swallowing. This
+        absence of evidence may be due to the small participant numbers in trials, resulting in insufficient power to
+        detect any difference. Data from the identified trials could not be combined due to differences in the choice
+        of primary outcomes and in the measurement tools used to assess them, and the differing baseline and endpoints
+        across studies. Designing and implementing studies with stronger methodological rigour is essential. There needs
+        to be agreement about the key primary outcomes, the choice of validated assessment tools to measure them and the
+        time points at which those measurements are made.",
+        "pls": "We included six studies with 326 participants who undertook therapeutic exercises before, during and/or
+        after HNC treatment. We could not combine the results of the studies because of the variation in participants'
+        cancers, their treatments, the outcomes measured and the tools used to assess them, as well as the differing
+        time points for testing. Researchers have compared: (i) therapeutic exercises versus treatment as usual (TAU);
+        (ii) therapeutic exercises versus sham therapy; (iii) therapeutic exercises plus TAU versus TAU. The therapeutic
+        exercises varied in their design, timing and intensity. TAU involved managing patients' dysphagia when it
+        occurred, including inserting a tube for non-oral feeding. The evidence is up to date to 1 July 2016. We found
+        no evidence that therapeutic exercises were better than TAU, or any other treatment, in improving the safety and
+        efficiency of oral swallowing (our primary outcome) or in improving any of the secondary outcomes. However,
+        there is insufficient evidence to draw any clear conclusion about the effects of undertaking therapeutic
+        exercises before during and/or immediately after HNC treatment on preventing or reducing dysphagia. Studies had
+        small participant numbers, used complex interventions and varied in the choice of outcomes measured, making it
+        difficult to draw reliable conclusions. There were no reported adverse events directly attributable to the
+        intervention (swallowing exercises). The current quality of the evidence to support the use of therapeutic
+        exercises before, during and/or immediately after HNC treatment to prevent/reduce dysphagia is very low. We need
+        better designed, rigorous studies with larger participant numbers and agreed endpoints and outcome measurements
+        in order to draw clear(er) conclusions."
+    },
+
+    where "pls" stands for "plain-language summary".
+
+    Paper: http://arxiv.org/abs/2104.05767
+    Code: https://github.com/AshOlogn/Paragraph-level-Simplification-of-Medical-Texts
+    """
+)
+
+# Link to an official homepage for the dataset here
+_HOMEPAGE = "https://github.com/AshOlogn/Paragraph-level-Simplification-of-Medical-Texts"
+
+_LICENSE = ""
+
+
+import datasets
+import os
+import json
+
+
+class Builder(datasets.GeneratorBasedBuilder):
+    VERSION = datasets.Version("1.0.0")
+
+    BUILDER_CONFIGS = [datasets.BuilderConfig(name="default", version=datasets.Version("1.0.0"), description=_DESCRIPTION)]
+
+    def _info(self):
+        features = datasets.Features(
+            {
+                "query": datasets.Value("string"),
+                "answer": datasets.Value("string"),
+            }
+        )
+        return datasets.DatasetInfo(
+            description=f"Covid Dialogue dataset, as preprocessed and shuffled in HELM",
+            features=features,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        test_target = dl_manager.download("test.source")
+        test_source = dl_manager.download("test.source")
+        train_source = dl_manager.download("train.source")
+        train_target = dl_manager.download("train.target")
+        val_source = dl_manager.download("valid.source")
+        val_target = dl_manager.download("valid.target")
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={"target": train_target, "source": train_source},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={"target": val_target, "source": val_source},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={"target": test_target, "source": test_source},
+            ),
+        ]
+
+    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
+    def _generate_examples(self, source, target):
+        with open(source, encoding="utf-8") as f_source:
+            with open(target, encoding="utf-8") as f_target:
+                for idx, (s, t) in enumerate(zip(f_source, f_target)):
+                    yield idx, {"query": s.rstrip(), "answer": t.rstrip()}