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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:** 8591.93 MB
-- **Size of the generated dataset:** 9622.19 MB
-- **Total amount of disk used:** 18214.12 MB
-
-### 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:** 4295.97 MB
-- **Size of the generated dataset:** 7231.70 MB
-- **Total amount of disk used:** 11527.66 MB
-
-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:** 4295.97 MB
-- **Size of the generated dataset:** 2390.49 MB
-- **Total amount of disk used:** 6686.46 MB
-
-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.

dataset_infos.json

@@ -1 +0,0 @@
-{"arxiv": {"description": "\nScientific papers datasets contains two sets of long and structured documents.\nThe datasets are obtained from ArXiv and PubMed OpenAccess repositories.\n\nBoth \"arxiv\" and \"pubmed\" have two features:\n  - article: the body of the document, pagragraphs seperated by \"/n\".\n  - abstract: the abstract of the document, pagragraphs seperated by \"/n\".\n  - section_names: titles of sections, seperated by \"/n\".\n\n", "citation": "\n@article{Cohan_2018,\n   title={A Discourse-Aware Attention Model for Abstractive Summarization of\n            Long Documents},\n   url={http://dx.doi.org/10.18653/v1/n18-2097},\n   DOI={10.18653/v1/n18-2097},\n   journal={Proceedings of the 2018 Conference of the North American Chapter of\n          the Association for Computational Linguistics: Human Language\n          Technologies, Volume 2 (Short Papers)},\n   publisher={Association for Computational Linguistics},\n   author={Cohan, Arman and Dernoncourt, Franck and Kim, Doo Soon and Bui, Trung and Kim, Seokhwan and Chang, Walter and Goharian, Nazli},\n   year={2018}\n}\n", "homepage": "https://github.com/armancohan/long-summarization", "license": "", "features": {"article": {"dtype": "string", "id": null, "_type": "Value"}, "abstract": {"dtype": "string", "id": null, "_type": "Value"}, "section_names": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "builder_name": "scientific_papers", "config_name": "arxiv", "version": {"version_str": "1.1.1", "description": null, "major": 1, "minor": 1, "patch": 1}, "splits": {"train": {"name": "train", "num_bytes": 7148341992, "num_examples": 203037, "dataset_name": "scientific_papers"}, "validation": {"name": "validation", "num_bytes": 217125524, "num_examples": 6436, "dataset_name": "scientific_papers"}, "test": {"name": "test", "num_bytes": 217514961, "num_examples": 6440, "dataset_name": "scientific_papers"}}, "download_checksums": {"https://s3.amazonaws.com/datasets.huggingface.co/scientific_papers/1.1.1/arxiv-dataset.zip": {"num_bytes": 3624420843, "checksum": "82ed30dd7c66a6497eeb3d7c3090c274e9e32c012438f8e0bb3cce3e6c1fcada"}, "https://s3.amazonaws.com/datasets.huggingface.co/scientific_papers/1.1.1/pubmed-dataset.zip": {"num_bytes": 880225504, "checksum": "d424074726a5e29e20bf834055fe7efe90f8a37bce0a2b512e4ab7e487013c04"}}, "download_size": 4504646347, "post_processing_size": null, "dataset_size": 7582982477, "size_in_bytes": 12087628824}, "pubmed": {"description": "\nScientific papers datasets contains two sets of long and structured documents.\nThe datasets are obtained from ArXiv and PubMed OpenAccess repositories.\n\nBoth \"arxiv\" and \"pubmed\" have two features:\n  - article: the body of the document, pagragraphs seperated by \"/n\".\n  - abstract: the abstract of the document, pagragraphs seperated by \"/n\".\n  - section_names: titles of sections, seperated by \"/n\".\n\n", "citation": "\n@article{Cohan_2018,\n   title={A Discourse-Aware Attention Model for Abstractive Summarization of\n            Long Documents},\n   url={http://dx.doi.org/10.18653/v1/n18-2097},\n   DOI={10.18653/v1/n18-2097},\n   journal={Proceedings of the 2018 Conference of the North American Chapter of\n          the Association for Computational Linguistics: Human Language\n          Technologies, Volume 2 (Short Papers)},\n   publisher={Association for Computational Linguistics},\n   author={Cohan, Arman and Dernoncourt, Franck and Kim, Doo Soon and Bui, Trung and Kim, Seokhwan and Chang, Walter and Goharian, Nazli},\n   year={2018}\n}\n", "homepage": "https://github.com/armancohan/long-summarization", "license": "", "features": {"article": {"dtype": "string", "id": null, "_type": "Value"}, "abstract": {"dtype": "string", "id": null, "_type": "Value"}, "section_names": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "builder_name": "scientific_papers", "config_name": "pubmed", "version": {"version_str": "1.1.1", "description": null, "major": 1, "minor": 1, "patch": 1}, "splits": {"train": {"name": "train", "num_bytes": 2252027383, "num_examples": 119924, "dataset_name": "scientific_papers"}, "validation": {"name": "validation", "num_bytes": 127403398, "num_examples": 6633, "dataset_name": "scientific_papers"}, "test": {"name": "test", "num_bytes": 127184448, "num_examples": 6658, "dataset_name": "scientific_papers"}}, "download_checksums": {"https://s3.amazonaws.com/datasets.huggingface.co/scientific_papers/1.1.1/arxiv-dataset.zip": {"num_bytes": 3624420843, "checksum": "82ed30dd7c66a6497eeb3d7c3090c274e9e32c012438f8e0bb3cce3e6c1fcada"}, "https://s3.amazonaws.com/datasets.huggingface.co/scientific_papers/1.1.1/pubmed-dataset.zip": {"num_bytes": 880225504, "checksum": "d424074726a5e29e20bf834055fe7efe90f8a37bce0a2b512e4ab7e487013c04"}}, "download_size": 4504646347, "post_processing_size": null, "dataset_size": 2506615229, "size_in_bytes": 7011261576}}

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scientific_papers.py

@@ -1,138 +0,0 @@
-# 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"]),
-                }