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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
"""NEWSROOM Dataset."""
import json
import os
import datasets
_CITATION = """
@inproceedings{N18-1065,
author = {Grusky, Max and Naaman, Mor and Artzi, Yoav},
title = {NEWSROOM: A Dataset of 1.3 Million Summaries
with Diverse Extractive Strategies},
booktitle = {Proceedings of the 2018 Conference of the
North American Chapter of the Association for
Computational Linguistics: Human Language Technologies},
year = {2018},
}
"""
_DESCRIPTION = """
NEWSROOM is a large dataset for training and evaluating summarization systems.
It contains 1.3 million articles and summaries written by authors and
editors in the newsrooms of 38 major publications.
Dataset features includes:
- text: Input news text.
- summary: Summary for the news.
And additional features:
- title: news title.
- url: url of the news.
- date: date of the article.
- density: extractive density.
- coverage: extractive coverage.
- compression: compression ratio.
- density_bin: low, medium, high.
- coverage_bin: extractive, abstractive.
- compression_bin: low, medium, high.
This dataset can be downloaded upon requests. Unzip all the contents
"train.jsonl, dev.josnl, test.jsonl" to the tfds folder.
"""
_DOCUMENT = "text"
_SUMMARY = "summary"
_ADDITIONAL_TEXT_FEATURES = [
"title",
"url",
"date",
"density_bin",
"coverage_bin",
"compression_bin",
]
_ADDITIONAL_FLOAT_FEATURES = [
"density",
"coverage",
"compression",
]
class Newsroom(datasets.GeneratorBasedBuilder):
"""NEWSROOM Dataset."""
VERSION = datasets.Version("1.0.0")
@property
def manual_download_instructions(self):
return """\
You should download the dataset from https://lil.nlp.cornell.edu/newsroom/download/index.html
The webpage requires registration.
To unzip the .tar file run `tar -zxvf complete.tar`. To unzip the .gz files
run `gunzip train.json.gz` , ...
After downloading, please put the files under the following names
dev.jsonl, test.jsonl and train.jsonl in a dir of your choice,
which will be used as a manual_dir, e.g. `~/.manual_dirs/newsroom`
Newsroom can then be loaded via:
`datasets.load_dataset("newsroom", data_dir="~/.manual_dirs/newsroom")`.
"""
def _info(self):
features = {k: datasets.Value("string") for k in [_DOCUMENT, _SUMMARY] + _ADDITIONAL_TEXT_FEATURES}
features.update({k: datasets.Value("float32") for k in _ADDITIONAL_FLOAT_FEATURES})
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(features),
supervised_keys=(_DOCUMENT, _SUMMARY),
homepage="https://lil.nlp.cornell.edu/newsroom/index.html",
citation=_CITATION,
)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
data_dir = os.path.abspath(os.path.expanduser(dl_manager.manual_dir))
if not os.path.exists(data_dir):
raise FileNotFoundError(
f"{data_dir} does not exist. Make sure you insert a manual dir via `datasets.load_dataset('newsroom', data_dir=...)` that includes files unzipped from the reclor zip. Manual download instructions: {self.manual_download_instructions}"
)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={"input_file": os.path.join(data_dir, "train.jsonl")},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={"input_file": os.path.join(data_dir, "dev.jsonl")},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={"input_file": os.path.join(data_dir, "test.jsonl")},
),
]
def _generate_examples(self, input_file=None):
"""Yields examples."""
with open(input_file, encoding="utf-8") as f:
for i, line in enumerate(f):
d = json.loads(line)
# fields are "url", "archive", "title", "date", "text",
# "compression_bin", "density_bin", "summary", "density",
# "compression', "coverage", "coverage_bin",
yield i, {
k: d[k] for k in [_DOCUMENT, _SUMMARY] + _ADDITIONAL_TEXT_FEATURES + _ADDITIONAL_FLOAT_FEATURES
}
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