Duplicate from armanc/scientific_papers

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README.md

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+---
+annotations_creators:
+- found
+language:
+- en
+language_creators:
+- found
+license:
+- unknown
+multilinguality:
+- monolingual
+pretty_name: ScientificPapers
+size_categories:
+- 100K<n<1M
+source_datasets:
+- original
+task_categories:
+- summarization
+task_ids: []
+paperswithcode_id: null
+tags:
+- abstractive-summarization
+dataset_info:
+- config_name: arxiv
+  features:
+  - name: article
+    dtype: string
+  - name: abstract
+    dtype: string
+  - name: section_names
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 7148341992
+    num_examples: 203037
+  - name: validation
+    num_bytes: 217125524
+    num_examples: 6436
+  - name: test
+    num_bytes: 217514961
+    num_examples: 6440
+  download_size: 4504646347
+  dataset_size: 7582982477
+- config_name: pubmed
+  features:
+  - name: article
+    dtype: string
+  - name: abstract
+    dtype: string
+  - name: section_names
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 2252027383
+    num_examples: 119924
+  - name: validation
+    num_bytes: 127403398
+    num_examples: 6633
+  - name: test
+    num_bytes: 127184448
+    num_examples: 6658
+  download_size: 4504646347
+  dataset_size: 2506615229
+---
+
+# Dataset Card for "scientific_papers"
+
+## Table of Contents
+- [Dataset Description](#dataset-description)
+  - [Dataset Summary](#dataset-summary)
+  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
+  - [Languages](#languages)
+- [Dataset Structure](#dataset-structure)
+  - [Data Instances](#data-instances)
+  - [Data Fields](#data-fields)
+  - [Data Splits](#data-splits)
+- [Dataset Creation](#dataset-creation)
+  - [Curation Rationale](#curation-rationale)
+  - [Source Data](#source-data)
+  - [Annotations](#annotations)
+  - [Personal and Sensitive Information](#personal-and-sensitive-information)
+- [Considerations for Using the Data](#considerations-for-using-the-data)
+  - [Social Impact of Dataset](#social-impact-of-dataset)
+  - [Discussion of Biases](#discussion-of-biases)
+  - [Other Known Limitations](#other-known-limitations)
+- [Additional Information](#additional-information)
+  - [Dataset Curators](#dataset-curators)
+  - [Licensing Information](#licensing-information)
+  - [Citation Information](#citation-information)
+  - [Contributions](#contributions)
+
+## Dataset Description
+
+- **Homepage:**
+- **Repository:** https://github.com/armancohan/long-summarization
+- **Paper:** [A Discourse-Aware Attention Model for Abstractive Summarization of Long Documents](https://arxiv.org/abs/1804.05685)
+- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+- **Size of downloaded dataset files:** 9.01 GB
+- **Size of the generated dataset:** 10.09 GB
+- **Total amount of disk used:** 19.10 GB
+
+### Dataset Summary
+
+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, paragraphs separated by "/n".
+  - abstract: the abstract of the document, paragraphs separated by "/n".
+  - section_names: titles of sections, separated by "/n".
+
+### Supported Tasks and Leaderboards
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+### Languages
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+## Dataset Structure
+
+### Data Instances
+
+#### arxiv
+
+- **Size of downloaded dataset files:** 4.50 GB
+- **Size of the generated dataset:** 7.58 GB
+- **Total amount of disk used:** 12.09 GB
+
+An example of 'train' looks as follows.
+```
+This example was too long and was cropped:
+
+{
+    "abstract": "\" we have studied the leptonic decay @xmath0 , via the decay channel @xmath1 , using a sample of tagged @xmath2 decays collected...",
+    "article": "\"the leptonic decays of a charged pseudoscalar meson @xmath7 are processes of the type @xmath8 , where @xmath9 , @xmath10 , or @...",
+    "section_names": "[sec:introduction]introduction\n[sec:detector]data and the cleo- detector\n[sec:analysys]analysis method\n[sec:conclusion]summary"
+}
+```
+
+#### pubmed
+
+- **Size of downloaded dataset files:** 4.50 GB
+- **Size of the generated dataset:** 2.51 GB
+- **Total amount of disk used:** 7.01 GB
+
+An example of 'validation' looks as follows.
+```
+This example was too long and was cropped:
+
+{
+    "abstract": "\" background and aim : there is lack of substantial indian data on venous thromboembolism ( vte ) . \\n the aim of this study was...",
+    "article": "\"approximately , one - third of patients with symptomatic vte manifests pe , whereas two - thirds manifest dvt alone .\\nboth dvt...",
+    "section_names": "\"Introduction\\nSubjects and Methods\\nResults\\nDemographics and characteristics of venous thromboembolism patients\\nRisk factors ..."
+}
+```
+
+### Data Fields
+
+The data fields are the same among all splits.
+
+#### arxiv
+- `article`: a `string` feature.
+- `abstract`: a `string` feature.
+- `section_names`: a `string` feature.
+
+#### pubmed
+- `article`: a `string` feature.
+- `abstract`: a `string` feature.
+- `section_names`: a `string` feature.
+
+### Data Splits
+
+| name |train |validation|test|
+|------|-----:|---------:|---:|
+|arxiv |203037|      6436|6440|
+|pubmed|119924|      6633|6658|
+
+## Dataset Creation
+
+### Curation Rationale
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+#### Who are the source language producers?
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+### Annotations
+
+#### Annotation process
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+#### Who are the annotators?
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+### Personal and Sensitive Information
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+### Discussion of Biases
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+### Other Known Limitations
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+## Additional Information
+
+### Dataset Curators
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+### Licensing Information
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+### Citation Information
+
+```
+@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}
+}
+```
+
+### Contributions
+
+Thanks to [@thomwolf](https://github.com/thomwolf), [@jplu](https://github.com/jplu), [@lewtun](https://github.com/lewtun), [@patrickvonplaten](https://github.com/patrickvonplaten) for adding this dataset.

scientific_papers.py

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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"]),
+                }