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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
"""Reddit TIFU dataset using tifu or tldr from subreddit tifu."""
import json
import datasets
_CITATION = """
@misc{kim2018abstractive,
title={Abstractive Summarization of Reddit Posts with Multi-level Memory Networks},
author={Byeongchang Kim and Hyunwoo Kim and Gunhee Kim},
year={2018},
eprint={1811.00783},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
"""
_DESCRIPTION = """
Reddit dataset, where TIFU denotes the name of subbreddit /r/tifu.
As defined in the publication, styel "short" uses title as summary and
"long" uses tldr as summary.
Features includes:
- document: post text without tldr.
- tldr: tldr line.
- title: trimmed title without tldr.
- ups: upvotes.
- score: score.
- num_comments: number of comments.
- upvote_ratio: upvote ratio.
"""
_URL = "data/tifu_all_tokenized_and_filtered.json.gz"
_DOCUMENT = "documents"
_TITLE = "title"
_TLDR = "tldr"
_ADDITIONAL_FEATURES = ["ups", "num_comments", "score", "upvote_ratio"]
class RedditTifuConfig(datasets.BuilderConfig):
"""BuilderConfig for RedditTifu."""
def __init__(self, summary_key=None, **kwargs):
"""BuilderConfig for RedditTifu.
Args:
summary_key: key string of summary in downloaded json file.
**kwargs: keyword arguments forwarded to super.
"""
# Version 1.1.0 remove empty document and summary strings.
super(RedditTifuConfig, self).__init__(version=datasets.Version("1.1.0"), **kwargs)
self.summary_key = summary_key
class RedditTifu(datasets.GeneratorBasedBuilder):
"""Reddit TIFU Dataset."""
BUILDER_CONFIGS = [
RedditTifuConfig(
name="short",
summary_key=_TITLE,
description="Using title as summary.",
),
RedditTifuConfig(
name="long",
summary_key=_TLDR,
description="Using TLDR as summary.",
),
]
def _info(self):
features = {
"ups": datasets.Value("float32"),
"num_comments": datasets.Value("float32"),
"upvote_ratio": datasets.Value("float32"),
"score": datasets.Value("float32"),
}
features.update({k: datasets.Value("string") for k in [_DOCUMENT, _TLDR, _TITLE]})
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(features),
supervised_keys=(_DOCUMENT, self.config.summary_key),
homepage="https://github.com/ctr4si/MMN",
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": dl_path},
)
]
def _generate_examples(self, path=None):
"""Yields examples."""
with open(path, "rb") as f:
for i, line in enumerate(f):
# keys are 'title_tokenized','permalink','title','url','num_comments',
# 'tldr'(optional),'created_utc','trimmed_title_tokenized','ups',
# 'selftext_html','score','upvote_ratio','tldr_tokenized'(optional),
# 'selftext','trimmed_title','selftext_without_tldr_tokenized',
# 'id','selftext_without_tldr'
d = json.loads(line)
r = {
_DOCUMENT: d["selftext_without_tldr"].strip(),
_TITLE: d["trimmed_title"].strip(),
_TLDR: (d["tldr"] or "").strip(),
}
r.update({k: d[k] for k in _ADDITIONAL_FEATURES})
# skip if document or summary is empty
if r[_DOCUMENT] and r[self.config.summary_key]:
yield i, r
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