Datasets:
Tasks:
Summarization
Modalities:
Text
Formats:
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Languages:
English
Size:
10K - 100K
ArXiv:
Tags:
aspect-based-summarization
conversations-summarization
multi-document-summarization
email-headline-generation
License:
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aeslc.py
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# coding=utf-8
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# Copyright 2020 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# Lint as: python3
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"""Annotated Enron Subject Line Corpus Dataset."""
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import glob
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import os
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import datasets
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_CITATION = """
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@misc{zhang2019email,
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title={This Email Could Save Your Life: Introducing the Task of Email Subject Line Generation},
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author={Rui Zhang and Joel Tetreault},
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year={2019},
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eprint={1906.03497},
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archivePrefix={arXiv},
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primaryClass={cs.CL}
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}
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"""
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_DESCRIPTION = """
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A collection of email messages of employees in the Enron Corporation.
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There are two features:
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- email_body: email body text.
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- subject_line: email subject text.
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"""
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# From: https://github.com/ryanzhumich/AESLC/archive/master.zip
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_URL = "data.zip"
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_DOCUMENT = "email_body"
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_SUMMARY = "subject_line"
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class Aeslc(datasets.GeneratorBasedBuilder):
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"""Annotated Enron Subject Line Corpus Dataset."""
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VERSION = datasets.Version("1.0.0")
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def _info(self):
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return datasets.DatasetInfo(
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description=_DESCRIPTION,
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features=datasets.Features({_DOCUMENT: datasets.Value("string"), _SUMMARY: datasets.Value("string")}),
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supervised_keys=(_DOCUMENT, _SUMMARY),
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homepage="https://github.com/ryanzhumich/AESLC",
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citation=_CITATION,
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)
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def _split_generators(self, dl_manager):
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"""Returns SplitGenerators."""
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dl_path = dl_manager.download_and_extract(_URL)
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input_path = os.path.join(dl_path, "AESLC-master", "enron_subject_line")
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return [
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datasets.SplitGenerator(
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name=datasets.Split.TRAIN,
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gen_kwargs={"pattern": os.path.join(input_path, "train", "*.subject")},
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),
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datasets.SplitGenerator(
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name=datasets.Split.VALIDATION,
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gen_kwargs={"pattern": os.path.join(input_path, "dev", "*.subject")},
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),
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datasets.SplitGenerator(
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name=datasets.Split.TEST,
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gen_kwargs={"pattern": os.path.join(input_path, "test", "*.subject")},
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),
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]
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def _generate_examples(self, pattern=None):
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"""Yields examples."""
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for filename in sorted(glob.glob(pattern)):
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email_body, subject_line = _parse_email_file(filename)
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key = os.path.basename(filename).rstrip(".subject")
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yield key, {_DOCUMENT: email_body, _SUMMARY: subject_line}
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def _parse_email_file(filename):
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"""Parse email file text for email body and subject."""
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with open(filename, encoding="utf-8") as f:
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email_body = ""
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for line in f:
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if line == "\n":
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break
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email_body += line
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line = next(f)
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subject = ""
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for line in f:
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if line == "\n":
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break
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subject += line
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return email_body, subject
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