import from S3

scientific_papers.py

@@ -0,0 +1,139 @@
+# 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."""
+
+from __future__ import absolute_import, division, print_function
+
+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/nlp/datasets/scientific_papers/dummy/arxiv/1.1.1/dummy_data.zip",
+    "pubmed": "https://s3.amazonaws.com/datasets.huggingface.co/nlp/datasets/scientific_papers/dummy/pubmed/1.1.1/dummy_data.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"]),
+                }

scientific_papers_dummy.py

@@ -0,0 +1,139 @@
+# 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."""
+
+from __future__ import absolute_import, division, print_function
+
+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/dummy_data/arxiv_data.zip",
+    "pubmed": "https://s3.amazonaws.com/datasets.huggingface.co/scientific_papers/1.1.1/dummy_data/pubmed_data.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], "dummy_data", self.config.name + "-dataset.zip", 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"]),
+                }