Datasets:

webis
/

tldr-17

	---
	annotations_creators:
	- no-annotation
	language_creators:
	- crowdsourced
	language:
	- en
	license:
	- cc-by-4.0
	multilinguality:
	- monolingual
	paperswithcode_id: webis-tldr-17-corpus
	pretty_name: Reddit Webis-TLDR-17
	size_categories:
	- 1M<n<10M
	source_datasets:
	- original
	task_categories:
	- summarization
	task_ids: []
	train-eval-index:
	- config: default
	task: summarization
	task_id: summarization
	splits:
	train_split: train
	col_mapping:
	content: text
	summary: target
	metrics:
	- type: rouge
	name: Rouge
	tags:
	- reddit-posts-summarization
	dataset_info:
	features:
	- name: author
	dtype: string
	- name: body
	dtype: string
	- name: normalizedBody
	dtype: string
	- name: subreddit
	dtype: string
	- name: subreddit_id
	dtype: string
	- name: id
	dtype: string
	- name: content
	dtype: string
	- name: summary
	dtype: string
	splits:
	- name: train
	num_bytes: 18940542951
	num_examples: 3848330
	download_size: 3141854161
	dataset_size: 18940542951
	---

	# Dataset Card for Reddit Webis-TLDR-17

	## Table of Contents
	- [Dataset Description](#dataset-description)
	- [Dataset Summary](#dataset-summary)
	- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
	- [Languages](#languages)
	- [Dataset Structure](#dataset-structure)
	- [Data Instances](#data-instances)
	- [Data Fields](#data-fields)
	- [Data Splits](#data-splits)
	- [Dataset Creation](#dataset-creation)
	- [Curation Rationale](#curation-rationale)
	- [Source Data](#source-data)
	- [Annotations](#annotations)
	- [Personal and Sensitive Information](#personal-and-sensitive-information)
	- [Considerations for Using the Data](#considerations-for-using-the-data)
	- [Social Impact of Dataset](#social-impact-of-dataset)
	- [Discussion of Biases](#discussion-of-biases)
	- [Other Known Limitations](#other-known-limitations)
	- [Additional Information](#additional-information)
	- [Dataset Curators](#dataset-curators)
	- [Licensing Information](#licensing-information)
	- [Citation Information](#citation-information)
	- [Contributions](#contributions)

	## Dataset Description

	- Homepage: [https://webis.de/data/webis-tldr-17.html](https://webis.de/data/webis-tldr-17.html)
	- Repository: [https://github.com/webis-de/webis-tldr-17-corpus](https://github.com/webis-de/webis-tldr-17-corpus)
	- Paper: [https://aclanthology.org/W17-4508]
	- Point of Contact: [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
	- Size of downloaded dataset files: 3.14 GB
	- Size of the generated dataset: 18.94 GB
	- Total amount of disk used: 22.08 GB

	### Dataset Summary

	This corpus contains preprocessed posts from the Reddit dataset (Webis-TLDR-17).
	The dataset consists of 3,848,330 posts with an average length of 270 words for content,
	and 28 words for the summary.

	Features includes strings: author, body, normalizedBody, content, summary, subreddit, subreddit_id.
	Content is used as document and summary is used as summary.

	### Supported Tasks and Leaderboards

	Summarization (abstractive)

	Known ROUGE scores achieved for the Webis-TLDR-17:

	\| Model \| ROUGE-1 \| ROUGE-2 \| ROUGE-L \| Paper/Source \|
	\|-------\|-------\|-------\|-------\|------:\|
	\| Transformer + Copy (Gehrmann et al., 2019) \| 22 \| 6 \| 17 \| Generating Summaries with Finetuned Language Models \|
	\| Unified VAE + PGN (Choi et al., 2019) \| 19 \| 4 \| 15 \| VAE-PGN based Abstractive Model in Multi-stage Architecture for Text Summarization \|

	(Source: https://github.com/sebastianruder/NLP-progress/blob/master/english/summarization.md)

	### Languages

	English

	## Dataset Structure

	### Data Instances

	#### default

	- Size of downloaded dataset files: 3.14 GB
	- Size of the generated dataset: 18.94 GB
	- Total amount of disk used: 22.08 GB

	An example of 'train' looks as follows.
	```
	{
	"author": "me",
	"body": "<>",
	"content": "input document.",
	"id": "1",
	"normalizedBody": "",
	"subreddit": "machinelearning",
	"subreddit_id": "2",
	"summary": "output summary."
	}
	```

	### Data Fields

	The data fields are the same among all splits.

	#### default
	- `author`: a `string` feature.
	- `body`: a `string` feature.
	- `normalizedBody`: a `string` feature.
	- `subreddit`: a `string` feature.
	- `subreddit_id`: a `string` feature.
	- `id`: a `string` feature.
	- `content`: a `string` feature.
	- `summary`: a `string` feature.

	### Data Splits

	\| name \| train \|
	\|-------\|------:\|
	\|default\|3848330\|

	This corpus does not contain a separate test set. Thus it is up to the users to divide the corpus into appropriate training, validation and test sets.

	## Dataset Creation

	### Curation Rationale

	In the scope of the task of absractive summarization the creators of the Webis-TLDR-17 propose mining social media for author-provided summaries and taking advantage of the common practice of appending a "TL;DR" to long posts. A large Reddit crawl was used to yield the Webis-TLDR-17 corpus. This dataset intends to complement the existing summarization corpora primarily from the news genre.

	### Source Data

	Reddit subreddits posts (submissions & comments) containing "TL;DR" from 2006 to 2016. Multiple subreddits are included.

	#### Initial Data Collection and Normalization

	Initial data: a set of 286 million submissions and 1.6 billion comments posted to Reddit between 2006 and 2016.
	Then a five-step pipeline of consecutive filtering steps was applied.

	#### Who are the source language producers?

	The contents of the dataset are produced by human authors, bot-generated content was eliminated by filtering out all bot accounts with the help of an extensive list provided by the Reddit community, as well as manual inspection of cases where the user name contained the substring "bot."

	### Annotations

	#### Annotation process

	[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

	#### Who are the annotators?

	[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

	### Personal and Sensitive Information

	[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

	## Considerations for Using the Data

	### Social Impact of Dataset

	This dataset has been created to serve as a source of large-scale summarization training data. It is primarily geared towards the automatic abstractive summarization task, that can be considered one of the most challenging variants of automatic summarization. It also aims to tackle the lack of genre diversity in the summarization datasets (most are news-related).

	### Discussion of Biases

	[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

	### Other Known Limitations

	Reddit users write TL;DRs with various intentions, such as providing a “true” summary, asking questions or for help, or forming judgments and conclusions. As noted in the paper introducing the dataset, although the first kind of TL;DR posts are most important for training summarization models, yet, the latter allow for various alternative summarization-related tasks.

	Although filtering was performed abusive language maybe still be present.

	## Additional Information

	### Dataset Curators

	Michael Völske, Martin Potthast, Shahbaz Syed, Benno Stein

	### Licensing Information

	[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

	### Citation Information

	```

	@inproceedings{volske-etal-2017-tl,
	title = "{TL};{DR}: Mining {R}eddit to Learn Automatic Summarization",
	author = {V{"o}lske, Michael and
	Potthast, Martin and
	Syed, Shahbaz and
	Stein, Benno},
	booktitle = "Proceedings of the Workshop on New Frontiers in Summarization",
	month = sep,
	year = "2017",
	address = "Copenhagen, Denmark",
	publisher = "Association for Computational Linguistics",
	url = "https://www.aclweb.org/anthology/W17-4508",
	doi = "10.18653/v1/W17-4508",
	pages = "59--63",
	abstract = "Recent advances in automatic text summarization have used deep neural networks to generate high-quality abstractive summaries, but the performance of these models strongly depends on large amounts of suitable training data. We propose a new method for mining social media for author-provided summaries, taking advantage of the common practice of appending a {``}TL;DR{''} to long posts. A case study using a large Reddit crawl yields the Webis-TLDR-17 dataset, complementing existing corpora primarily from the news genre. Our technique is likely applicable to other social media sites and general web crawls.",
	}

	```


	### Contributions

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