File size: 3,681 Bytes
4f7eb0e 9277389 4f7eb0e 9277389 4f7eb0e 965a5f1 4f7eb0e bf5b7e9 4f7eb0e bf5b7e9 4f7eb0e 7f90eef 326022e 7f90eef a4f428a ae06cda 7f90eef c53ea61 759599c ae06cda c53ea61 92fc6d2 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 |
# 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-Document Dataset."""
import json
import datasets
from datasets import set_caching_enabled
set_caching_enabled(False)
_CITATION = """
@article{lu2020multi,
title={Multi-Document: A Large-scale Dataset for Extreme Multi-document Summarization of Scientific Articles},
author={Arka Das, India},
journal={arXiv preprint arXiv:2010.14235},
year={2022}
}
"""
_DESCRIPTION = """
Multi-Document, a large-scale multi-document summarization dataset created from scientific articles. Multi-Document introduces a challenging multi-document summarization task: writing the related-work section of a paper based on its abstract and the articles it references.
"""
_URL_TRAIN = "https://github.com/arka0821/multi_document_summarization/raw/master/data/train.json.gz"
_URL_TEST = "https://github.com/arka0821/multi_document_summarization/raw/master/data/test.json.gz"
_URL_VAL = "https://github.com/arka0821/multi_document_summarization/raw/master/data/val.json.gz"
class MultiDocumentSum(datasets.GeneratorBasedBuilder):
""" "Multi-Document Dataset."""
VERSION = datasets.Version("1.1.0")
def _info(selif):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"id": datasets.Value("string"),
"docs": datasets.Sequence(
{
"id": datasets.Value("string"),
"text": datasets.Value("string")
},
),
"summary": datasets.Value("string"),
}
),
supervised_keys=None,
homepage="https://github.com/arka0821/multi_document_summarization",
citation=_CITATION,
)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
train_path = dl_manager.download_and_extract(_URL_TRAIN)
test_path = dl_manager.download_and_extract(_URL_TEST)
val_path = dl_manager.download_and_extract(_URL_VAL)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={"path": train_path},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={"path": test_path},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={"path": val_path},
),
]
def _generate_examples(self, path=None):
"""Yields examples."""
with open(path, encoding="utf-8") as f:
data = json.load(f)
f.close()
for idx, el in enumerate(data):
ids = [id["id"] for id in el["docs"]]
texts = [text["text"] for text in el["docs"]]
tmp = {"id": ids, "text": texts}
d = el.copy()
d["docs"] = tmp
yield idx, d
|