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Datasets:
webis
/
tldr-17

Tasks:
Summarization
Modalities:
Text
Languages:
English
Size:
1M - 10M
Tags:
reddit-posts-summarization
Libraries:
Datasets
License:
Dataset card Viewer Files Community
8
tldr-17
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---
---

# Dataset Card for "reddit"

## Table of Contents
- [Dataset Description](#dataset-description)
  - [Dataset Summary](#dataset-summary)
  - [Supported Tasks](#supported-tasks)
  - [Languages](#languages)
- [Dataset Structure](#dataset-structure)
  - [Data Instances](#data-instances)
  - [Data Fields](#data-fields)
  - [Data Splits Sample Size](#data-splits-sample-size)
- [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](#dataset-description)

- **Homepage:** [https://github.com/webis-de/webis-tldr-17-corpus](https://github.com/webis-de/webis-tldr-17-corpus)
- **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **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:** 2996.31 MB
- **Size of the generated dataset:** 18063.11 MB
- **Total amount of disk used:** 21059.41 MB

### [Dataset Summary](#dataset-summary)

This corpus contains preprocessed posts from the Reddit dataset.
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](#supported-tasks)

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

### [Languages](#languages)

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

## [Dataset Structure](#dataset-structure)

We show detailed information for up to 5 configurations of the dataset.

### [Data Instances](#data-instances)

#### default

- **Size of downloaded dataset files:** 2996.31 MB
- **Size of the generated dataset:** 18063.11 MB
- **Total amount of disk used:** 21059.41 MB

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](#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 Sample Size](#data-splits-sample-size)

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

## [Dataset Creation](#dataset-creation)

### [Curation Rationale](#curation-rationale)

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

### [Source Data](#source-data)

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

### [Annotations](#annotations)

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

### [Personal and Sensitive Information](#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](#considerations-for-using-the-data)

### [Social Impact of Dataset](#social-impact-of-dataset)

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

### [Discussion of Biases](#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](#other-known-limitations)

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

## [Additional Information](#additional-information)

### [Dataset Curators](#dataset-curators)

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

### [Licensing Information](#licensing-information)

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

### [Citation Information](#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.