region:us
WikiHow is a new large-scale dataset using the online WikiHow
(http://www.wikihow.com/) knowledge base.

There are two features:
  - text: wikihow answers texts.
  - headline: bold lines as summary.

There are two separate versions:
  - all: consisting of the concatenation of all paragraphs as the articles and
         the bold lines as the reference summaries.
  - sep: consisting of each paragraph and its summary.

Download "wikihowAll.csv" and "wikihowSep.csv" from
https://github.com/mahnazkoupaee/WikiHow-Dataset and place them in manual folder
https://www.tensorflow.org/datasets/api_docs/python/tfds/download/DownloadConfig.
Train/validation/test splits are provided by the authors.
Preprocessing is applied to remove short articles
(abstract length < 0.75 article length) and clean up extra commas.
@misc{koupaee2018wikihow,
    title={WikiHow: A Large Scale Text Summarization Dataset},
    author={Mahnaz Koupaee and William Yang Wang},
    year={2018},
    eprint={1810.09305},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}
---
paperswithcode_id: wikihow
pretty_name: WikiHow
dataset_info:
- config_name: all
  features:
  - name: text
    dtype: string
  - name: headline
    dtype: string
  - name: title
    dtype: string
  splits:
  - name: train
    num_bytes: 513238309
    num_examples: 157252
  - name: validation
    num_bytes: 18246897
    num_examples: 5599
  - name: test
    num_bytes: 18276023
    num_examples: 5577
  download_size: 5460385
  dataset_size: 549761229
- config_name: sep
  features:
  - name: text
    dtype: string
  - name: headline
    dtype: string
  - name: title
    dtype: string
  - name: overview
    dtype: string
  - name: sectionLabel
    dtype: string
  splits:
  - name: train
    num_bytes: 990499776
    num_examples: 1060732
  - name: validation
    num_bytes: 35173966
    num_examples: 37932
  - name: test
    num_bytes: 35271826
    num_examples: 37800
  download_size: 5460385
  dataset_size: 1060945568
---

### Contributions

Thanks to [@thomwolf](https://github.com/thomwolf), [@lewtun](https://github.com/lewtun), [@patrickvonplaten](https://github.com/patrickvonplaten) for adding this dataset.