README.md

metadata

language:
  - en
license:
  - mit
multilinguality:
  - monolingual
pretty_name: AESLC - Cleaned & Keyword Extracted
source_datasets:
  - aeslc
tags:
  - text2text generation
  - email
  - email generation
  - enron

about

• aeslc dataset but cleaned and keywords extracted to a new column
• an EDA website generated via pandas profiling is on netlify here

DatasetDict({
    train: Dataset({
        features: ['email_body', 'subject_line', 'clean_email', 'clean_email_keywords'],
        num_rows: 14436
    })
    test: Dataset({
        features: ['email_body', 'subject_line', 'clean_email', 'clean_email_keywords'],
        num_rows: 1906
    })
    validation: Dataset({
        features: ['email_body', 'subject_line', 'clean_email', 'clean_email_keywords'],
        num_rows: 1960
    })
})

Python usage

Basic example notebook here.

from datasets import load_dataset

dataset = load_dataset("postbot/aeslc_kw")

Citation

@InProceedings{zhang2019slg,
  author =      "Rui Zhang and Joel Tetreault",
  title =       "This Email Could Save Your Life: Introducing the Task of Email Subject Line Generation",
  booktitle =   "Proceedings of The 57th Annual Meeting of the Association for Computational Linguistics",
  year =        "2019",
  address =     "Florence, Italy"
}