Prepare to rename to tldr-17 (#8)

- Copy reddit.py to tldr-17.py (d3ca8af1dc1a62ea58f2f093140e7d09a08a2f49)

tldr-17.py

@@ -0,0 +1,101 @@
+# 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
+"""Reddit dataset using tldr as summaries."""
+
+import json
+import os
+
+import datasets
+
+
+_CITATION = """
+@inproceedings{volske-etal-2017-tl,
+    title = {TL;DR: Mining {R}eddit to Learn Automatic Summarization},
+    author = {V{\"o}lske, Michael  and Potthast, Martin  and Syed, Shahbaz  and Stein, Benno},
+    booktitle = {Proceedings of the Workshop on New Frontiers in Summarization},
+    month = {sep},
+    year = {2017},
+    address = {Copenhagen, Denmark},
+    publisher = {Association for Computational Linguistics},
+    url = {https://www.aclweb.org/anthology/W17-4508},
+    doi = {10.18653/v1/W17-4508},
+    pages = {59--63},
+    abstract = {Recent advances in automatic text summarization have used deep neural networks to generate high-quality abstractive summaries, but the performance of these models strongly depends on large amounts of suitable training data. We propose a new method for mining social media for author-provided summaries, taking advantage of the common practice of appending a {``}TL;DR{''} to long posts. A case study using a large Reddit crawl yields the Webis-TLDR-17 dataset, complementing existing corpora primarily from the news genre. Our technique is likely applicable to other social media sites and general web crawls.},
+}
+"""
+
+_DESCRIPTION = """
+This corpus contains preprocessed posts from the Reddit dataset.
+The dataset consists of 3,848,330 posts with an average length of 270 words for content,
+and 28 words for the summary.
+
+Features includes strings: author, body, normalizedBody, content, summary, subreddit, subreddit_id.
+Content is used as document and summary is used as summary.
+"""
+
+_URL = "data/corpus-webis-tldr-17.zip"
+
+_DOCUMENT = "content"
+_SUMMARY = "summary"
+_ADDITIONAL_FEATURES = ["author", "body", "normalizedBody", "subreddit", "subreddit_id", "id"]
+
+
+class Reddit(datasets.GeneratorBasedBuilder):
+    """Reddit Dataset."""
+
+    VERSION = datasets.Version("1.0.0")
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {k: datasets.Value("string") for k in _ADDITIONAL_FEATURES + [_DOCUMENT, _SUMMARY]}
+            ),
+            supervised_keys=None,
+            homepage="https://github.com/webis-de/webis-tldr-17-corpus",
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        dl_path = dl_manager.download_and_extract(_URL)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={"path": os.path.join(dl_path, "corpus-webis-tldr-17.json")},
+            )
+        ]
+
+    def _generate_examples(self, path=None):
+        """Yields examples."""
+        with open(path, "rb") as f:
+            for i, line in enumerate(f):
+                # possible keys are:
+                #   author: string (nullable = true)
+                #   body: string (nullable = true)
+                #   normalizedBody: string (nullable = true)
+                #   content: string (nullable = true)
+                #   content_len: long (nullable = true)
+                #   summary: string (nullable = true)
+                #   summary_len: long (nullable = true)
+                #   id: string (nullable = true)
+                #   subreddit: string (nullable = true)
+                #   subreddit_id: string (nullable = true)
+                #   title: string (nullable = true)
+                d = json.loads(line)
+                if _SUMMARY in d and _DOCUMENT in d:
+                    yield i, {k: d.get(k, "") for k in _ADDITIONAL_FEATURES + [_DOCUMENT, _SUMMARY]}