Datasets:
Tasks:
Summarization
Sub-tasks:
news-articles-summarization
Languages:
English
Size:
10K<n<100K
ArXiv:
License:
File size: 3,521 Bytes
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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
"""Multi-News dataset."""
import os
import datasets
_CITATION = """
@misc{alex2019multinews,
title={Multi-News: a Large-Scale Multi-Document Summarization Dataset and Abstractive Hierarchical Model},
author={Alexander R. Fabbri and Irene Li and Tianwei She and Suyi Li and Dragomir R. Radev},
year={2019},
eprint={1906.01749},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
"""
_DESCRIPTION = """
Multi-News, consists of news articles and human-written summaries
of these articles from the site newser.com.
Each summary is professionally written by editors and
includes links to the original articles cited.
There are two features:
- document: text of news articles seperated by special token "|||||".
- summary: news summary.
"""
_URL = "https://drive.google.com/uc?export=download&id=1vRY2wM6rlOZrf9exGTm5pXj5ExlVwJ0C"
_DOCUMENT = "document"
_SUMMARY = "summary"
class MultiNews(datasets.GeneratorBasedBuilder):
"""Multi-News dataset."""
VERSION = datasets.Version("1.0.0")
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features({_DOCUMENT: datasets.Value("string"), _SUMMARY: datasets.Value("string")}),
supervised_keys=(_DOCUMENT, _SUMMARY),
homepage="https://github.com/Alex-Fabbri/Multi-News",
citation=_CITATION,
)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
extract_path = os.path.join(dl_manager.download_and_extract(_URL), "multi-news-original")
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={"path": os.path.join(extract_path, "train")},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={"path": os.path.join(extract_path, "val")},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={"path": os.path.join(extract_path, "test")},
),
]
def _generate_examples(self, path=None):
"""Yields examples."""
with open(os.path.join(path + ".src"), encoding="utf-8") as src_f, open(
os.path.join(path + ".tgt"), encoding="utf-8"
) as tgt_f:
for i, (src_line, tgt_line) in enumerate(zip(src_f, tgt_f)):
yield i, {
# In original file, each line has one example and natural newline
# tokens "\n" are being replaced with "NEWLINE_CHAR". Here restore
# the natural newline token to avoid special vocab "NEWLINE_CHAR".
_DOCUMENT: src_line.strip().replace("NEWLINE_CHAR", "\n"),
_SUMMARY: tgt_line.strip(),
}
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