Datasets:
Tasks:
Summarization
Languages:
English
Multilinguality:
monolingual
Size Categories:
10K<n<100K
Language Creators:
found
Annotations Creators:
crowdsourced
Source Datasets:
original
ArXiv:
Tags:
aspect-based-summarization
conversations-summarization
multi-document-summarization
email-headline-generation
License:
File size: 3,459 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
"""Annotated Enron Subject Line Corpus Dataset."""
import glob
import os
import datasets
_CITATION = """
@misc{zhang2019email,
title={This Email Could Save Your Life: Introducing the Task of Email Subject Line Generation},
author={Rui Zhang and Joel Tetreault},
year={2019},
eprint={1906.03497},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
"""
_DESCRIPTION = """
A collection of email messages of employees in the Enron Corporation.
There are two features:
- email_body: email body text.
- subject_line: email subject text.
"""
# From: https://github.com/ryanzhumich/AESLC/archive/master.zip
_URL = "data.zip"
_DOCUMENT = "email_body"
_SUMMARY = "subject_line"
class Aeslc(datasets.GeneratorBasedBuilder):
"""Annotated Enron Subject Line Corpus 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/ryanzhumich/AESLC",
citation=_CITATION,
)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
dl_path = dl_manager.download_and_extract(_URL)
input_path = os.path.join(dl_path, "AESLC-master", "enron_subject_line")
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={"pattern": os.path.join(input_path, "train", "*.subject")},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={"pattern": os.path.join(input_path, "dev", "*.subject")},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={"pattern": os.path.join(input_path, "test", "*.subject")},
),
]
def _generate_examples(self, pattern=None):
"""Yields examples."""
for filename in sorted(glob.glob(pattern)):
email_body, subject_line = _parse_email_file(filename)
key = os.path.basename(filename).rstrip(".subject")
yield key, {_DOCUMENT: email_body, _SUMMARY: subject_line}
def _parse_email_file(filename):
"""Parse email file text for email body and subject."""
with open(filename, encoding="utf-8") as f:
email_body = ""
for line in f:
if line == "\n":
break
email_body += line
line = next(f)
subject = ""
for line in f:
if line == "\n":
break
subject += line
return email_body, subject
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