add base code for elsevier-oa-cc-by corpus

LICENCE.md

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+CC BY 4.0
+
+You can share, copy and modify this dataset so long as you give appropriate credit, provide a link to the CC BY license, and indicate if changes were made, but you may not do so in a way that suggests the rights holder has endorsed you or your use of the dataset. Note that further permission may be required for any content within the dataset that is identified as belonging to a third party.

README.md

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 ---
-license: cc-by-4.0
+annotations_creators:
+- expert-generated
+language_creators:
+- expert-generated
+languages:
+- en
+licenses:
+- cc-by-4.0
+multilinguality:
+- monolingual
+pretty_name: Elsevier OA CC-By Corpus
+paperswithcode_id: elsevier-oa-cc-by-corpus
+size_categories:
+- 10K<n<100K
+source_datasets:
+- original
+task_categories:
+- fill-mask
+- summarization
+- text-classification
+task_ids:
+- masked-language-modeling
+- news-articles-summarization
+- news-articles-headline-generation
 ---
+
+# Dataset Card for [Dataset Name]
+
+## Table of Contents
+- [Dataset Card for [Dataset Name]](#dataset-card-for-dataset-name)
+  - [Table of Contents](#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)
+      - [Initial Data Collection and Normalization](#initial-data-collection-and-normalization)
+      - [Who are the source language producers?](#who-are-the-source-language-producers)
+    - [Annotations](#annotations)
+      - [Annotation process](#annotation-process)
+      - [Who are the annotators?](#who-are-the-annotators)
+    - [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:** https://elsevier.digitalcommonsdata.com/datasets/zm33cdndxs
+- **Repository:** https://elsevier.digitalcommonsdata.com/datasets/zm33cdndxs
+- **Paper:** https://arxiv.org/abs/2008.00774
+- **Leaderboard:**
+- **Point of Contact:** [@orieg](https://huggingface.co/orieg)
+
+### Dataset Summary
+
+Elsevier OA CC-By Corpus: This is a corpus of 40k (40, 091) open access (OA) CC-BY articles from across Elsevier’s journals
+representing a large scale, cross-discipline set of research data to support NLP and ML research.
+
+***docId*** The docID is the identifier of the document. This is unique to the document, and can be resolved into a URL
+for the document through the addition of https//www.sciencedirect.com/science/pii/<docId>
+
+***abstract*** This is the author provided abstract for the document
+body_text The full text for the document. The text has been split on sentence boundaries, thus making it easier to
+use across research projects. Each sentence has the title (and ID) of the section which it is from, along with titles (and
+IDs) of the parent section. The highest-level section takes index 0 in the parents array. If the array is empty then the
+title of the section for the sentence is the highest level section title. This will allow for the reconstruction of the article
+structure. References have been extracted from the sentences. The IDs of the extracted reference and their respective
+offset within the sentence can be found in the “refoffsets” field. The complete list of references are can be found in
+the “bib_entry” field along with the references’ respective metadata. Some will be missing as we only keep ‘clean’
+sentences,
+
+***bib_entities*** All the references from within the document can be found in this section. If the meta data for the
+reference is available, it has been added against the key for the reference. Where possible information such as the
+document titles, authors, and relevant identifiers (DOI and PMID) are included. The keys for each reference can be
+found in the sentence where the reference is used with the start and end offset of where in the sentence that reference
+was used.
+
+***metadata*** Meta data includes additional information about the article, such as list of authors, relevant IDs (DOI and
+PMID). Along with a number of classification schemes such as ASJC and Subject Classification.
+
+***Author_highlights*** Author highlights were included in the corpus where the author(s) have provided them. The
+coverage is 61% of all articles. The author highlights, consisting of 4 to 6 sentences, is provided by the author with
+the aim of summarising the core findings and results in the article.
+
+### Supported Tasks and Leaderboards
+
+[More Information Needed]
+
+### Languages
+
+English (`en`).
+
+## Dataset Structure
+
+* ***title***:This is the author provided title for the document. 100% coverage.
+* ***abstract***: This is the author provided abstract for the document. 99.25% coverage.
+* ***keywords***: This is the author and publisher provided keywords for the document. 100% coverage.
+* ***asjc***: This is the disciplines for the document as represented by 334 ASJC (All Science Journal Classification) codes. 100% coverage.
+* ***subjareas***: This is the Subject Classification for the document as represented by 27 ASJC top-level subject classifications. 100% coverage.
+* ***body_text***: The full text for the document. 100% coverage.
+* ***author_highlights***: This is the author provided highlights for the document. 61.31% coverage.
+    
+### Data Instances
+
+The original dataset was published with the following json structure:
+```
+{
+    "docId": <str>,
+    "metadata":{
+        "title": <str>,
+        "authors": [
+            {
+                "first": <str>,
+                "initial": <str>,
+                "last": <str>,
+                "email": <str>
+            },
+            ...
+        ],
+        "issn": <str>,
+        "volume": <str>,
+        "firstpage": <str>,
+        "lastpage": <str>,
+        "pub_year": <int>,
+        "doi": <str>,
+        "pmid": <str>,
+        "openaccess": "Full",
+        "subjareas": [<str>],
+        "keywords": [<str>],
+        "asjc": [<int>],
+    },
+    "abstract":[
+        {
+          "sentence": <str>,
+          "startOffset": <int>,
+          "endOffset": <int>
+        },
+        ...
+    ],
+    "bib_entries":{
+        "BIBREF0":{
+            "title":<str>,
+            "authors":[
+                {
+                "last":<str>,
+                "initial":<str>,
+                "first":<str>
+                },
+                ...
+            ],
+            "issn": <str>,
+            "volume": <str>,
+            "firstpage": <str>,
+            "lastpage": <str>,
+            "pub_year": <int>,
+            "doi": <str>,
+            "pmid": <str>
+        },
+        ...
+    },
+    "body_text":[
+        {
+        "sentence": <str>,
+        "secId": <str>,
+        "startOffset": <int>,
+        "endOffset": <int>,
+        "title": <str>,
+        "refoffsets": {
+            <str>:{
+                "endOffset":<int>,
+                "startOffset":<int>
+                }
+            },
+        "parents": [
+            {
+            "id": <str>,
+            "title": <str>
+            },
+            ...
+        ]
+    },
+    ...
+    ]
+}
+```
+
+### Data Fields
+
+[More Information Needed]
+
+### Data Splits
+
+[More Information Needed]
+
+## Dataset Creation
+
+### Curation Rationale
+
+See `3.1 Data Sampling` in the [original paper](https://doi.org/10.48550/arXiv.2008.00774).
+
+[More Information Needed]
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+Date the data was collected:	2020-06-25T11:00:00.000Z
+
+[More Information Needed]
+
+#### Who are the source language producers?
+
+[More Information Needed]
+
+### Annotations
+
+#### Annotation process
+
+[More Information Needed]
+
+#### Who are the annotators?
+
+[More Information Needed]
+
+### Personal and Sensitive Information
+
+[More Information Needed]
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+[More Information Needed]
+
+### Discussion of Biases
+
+[More Information Needed]
+
+### Other Known Limitations
+
+[More Information Needed]
+
+## Additional Information
+
+### Dataset Curators
+
+[More Information Needed]
+
+### Licensing Information
+
+[CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)
+
+### Citation Information
+
+```
+@article{Kershaw2020ElsevierOC,
+  title     = {Elsevier OA CC-By Corpus},
+  author    = {Daniel James Kershaw and R. Koeling},
+  journal   = {ArXiv},
+  year      = {2020},
+  volume    = {abs/2008.00774},
+  doi       = {https://doi.org/10.48550/arXiv.2008.00774},
+  url       = {https://elsevier.digitalcommonsdata.com/datasets/zm33cdndxs},
+  keywords  = {Science, Natural Language Processing, Machine Learning, Open Dataset},
+  abstract  = {We introduce the Elsevier OA CC-BY corpus. This is the first open
+               corpus of Scientific Research papers which has a representative sample
+               from across scientific disciplines. This corpus not only includes the
+               full text of the article, but also the metadata of the documents, 
+               along with the bibliographic information for each reference.}
+}
+```
+
+```
+@dataset{https://10.17632/zm33cdndxs.3,
+  doi       = {10.17632/zm33cdndxs.2},
+  url       = {https://data.mendeley.com/datasets/zm33cdndxs/3},
+  author    = "Daniel Kershaw and Rob Koeling",
+  keywords  = {Science, Natural Language Processing, Machine Learning, Open Dataset},
+  title     = {Elsevier OA CC-BY Corpus},
+  publisher = {Mendeley},
+  year      = {2020},
+  month     = {sep}
+}
+```
+
+### Contributions
+
+Thanks to [@orieg](https://github.com/orieg) for adding this dataset.

elsevier-oa-cc-by.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
+"""Elsevier OA CC-By Corpus Dataset."""
+
+
+
+import json
+import glob
+import os
+import math
+
+import datasets
+
+
+_CITATION = """
+@article{Kershaw2020ElsevierOC,
+  title     = {Elsevier OA CC-By Corpus},
+  author    = {Daniel James Kershaw and R. Koeling},
+  journal   = {ArXiv},
+  year      = {2020},
+  volume    = {abs/2008.00774},
+  doi       = {https://doi.org/10.48550/arXiv.2008.00774},
+  url       = {https://elsevier.digitalcommonsdata.com/datasets/zm33cdndxs},
+  keywords  = {Science, Natural Language Processing, Machine Learning, Open Dataset},
+  abstract  = {We introduce the Elsevier OA CC-BY corpus. This is the first open
+               corpus of Scientific Research papers which has a representative sample
+               from across scientific disciplines. This corpus not only includes the
+               full text of the article, but also the metadata of the documents, 
+               along with the bibliographic information for each reference.}
+}
+"""
+
+_DESCRIPTION = """
+Elsevier OA CC-By is a corpus of 40k (40, 091) open access (OA) CC-BY articles
+from across Elsevier’s journals and include the full text of the article, the metadata,
+the bibliographic information for each reference, and author highlights.
+"""
+
+_HOMEPAGE = "https://elsevier.digitalcommonsdata.com/datasets/zm33cdndxs/3"
+
+_LICENSE = "CC-BY-4.0"
+
+_URLS = {
+    "mendeley": "https://data.mendeley.com/public-files/datasets/zm33cdndxs/files/4e03ae48-04a7-44d4-b103-ce73e548679c/file_downloaded"
+}
+
+
+class ElsevierOaCcBy(datasets.GeneratorBasedBuilder):
+    """Elsevier OA CC-By Dataset."""
+
+    VERSION = datasets.Version("1.0.0")
+
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(name="mendeley", version=VERSION, description="Official Mendeley dataset"),
+    ]
+
+    DEFAULT_CONFIG_NAME = "mendeley"
+
+    def _info(self):
+        features = datasets.Features(
+            {
+                "title": datasets.Value("string"),
+                "abstract": datasets.Value("string"),
+                "subjareas": datasets.Sequence(datasets.Value("string")),
+                "keywords": datasets.Sequence(datasets.Value("string")),
+                "asjc": datasets.Sequence(datasets.Value("string")),
+                "body_text": datasets.Sequence(datasets.Value("string")),
+                "author_highlights": datasets.Sequence(datasets.Value("string")),
+            }
+        )
+        return datasets.DatasetInfo(
+            # This is the description that will appear on the datasets page.
+            description=_DESCRIPTION,
+            # This defines the different columns of the dataset and their types
+            features=features,  # Here we define them above because they are different between the two configurations
+            # If there's a common (input, target) tuple from the features, uncomment supervised_keys line below and
+            # specify them. They'll be used if as_supervised=True in builder.as_dataset.
+            # supervised_keys=("sentence", "label"),
+            # Homepage of the dataset for documentation
+            homepage=_HOMEPAGE,
+            # License for the dataset if available
+            license=_LICENSE,
+            # Citation for the dataset
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        # TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
+        # If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name
+
+        # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLS
+        # It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
+        # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
+        urls = _URLS[self.config.name]
+        data_dir = dl_manager.download_and_extract(urls)
+        
+        corpus_path = os.path.join(data_dir, "json")
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepath": corpus_path,
+                    "split": "train",
+                    "split_range": [0, 32072]
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepath": corpus_path,
+                    "split": "test",
+                    "split_range": [32073, 36082]
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepath": corpus_path,
+                    "split": "validation",
+                    "split_range": [36083, 40091]
+                },
+            ),
+        ]
+
+    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
+    def _generate_examples(self, filepath, split, split_range):
+        # TODO: This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
+        # The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
+        json_files = glob.glob(f"{filepath}/*.json")
+        for doc in json_files[split_range[0]:split_range[1]]:
+            with open(doc) as f:
+                paper = json.loads(f.read())
+                # Yields examples as (key, example) tuples
+                yield paper['docId'], {
+                    'title': paper['metadata']['title'],
+                    'subjareas': paper['metadata']['subjareas'] if 'subjareas' in paper['metadata'] else [],
+                    'keywords': paper['metadata']['keywords'] if 'keywords' in paper['metadata'] else [],
+                    'asjc': paper['metadata']['asjc'] if 'asjc' in paper['metadata'] else [],
+                    'abstract': paper['abstract'] if 'abstract' in paper else "",
+                    "body_text": [s['sentence'] for s in sorted(paper['body_text'], key = lambda i: (i['secId'], i['startOffset']))],
+                    "author_highlights": [s['sentence'] for s in sorted(paper['author_highlights'], key = lambda i: i['startOffset'])] if 'author_highlights' in paper else [],
+                }