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---
annotations_creators:
- other
language_creators:
- other
languages:
- en
licenses:
- mit
multilinguality:
- monolingual
pretty_name: tldr_news
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- summarization
- text2text-generation
- text-generation
task_ids:
- news-articles-headline-generation
- text-simplification
- language-modeling
---
# Dataset Card for `tldr_news`
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-instances)
- [Data Splits](#data-instances)
- [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)
## Dataset Description
- **Homepage:** https://tldr.tech/newsletter
### Dataset Summary
The `tldr_news` dataset was constructed by collecting a daily tech newsletter (available
[here](https://tldr.tech/newsletter)). Then, for every piece of news, the `headline` and its corresponding `
content` were extracted.
Such a dataset can be used to train a model to generate a headline from a input piece of text.
### Supported Tasks and Leaderboards
There is no official supported tasks nor leaderboard for this dataset. However, it could be used for the following
tasks:
- summarization
- headline generation
### Languages
en
## Dataset Structure
### Data Instances
A data point comprises a "headline" and its corresponding "content".
An example is as follows:
```
{
"headline": "World-first 'impossible' rotating detonation engine fires up",
"content": "A team in Florida working with the US Air Force has built an experimental model of a rotating detonation rocket engine. It uses spinning explosions inside a ring channel to create a super-efficient thrust. Most engines use combustion, which is a relatively slow and controlled process compared to detonation. Detonation releases significantly more energy than combustion with significantly less fuel, but detonation engines have proven to be incredibly difficult to build and sustain. A 20-second slow-motion video of the rocket firing is available."
}
```
### Data Fields
- `headline (str)`: the piece of news' headline
- `content (str)`: the piece of news
### Data Splits
- `all`: all existing daily newsletters available [here](https://tldr.tech/newsletter).
## Dataset Creation
### Curation Rationale
This dataset was obtained by scrapping the collecting all the existing newsletter
available [here](https://tldr.tech/newsletter).
Every single newsletter was then processed to extract all the different pieces of news. Then for every collected piece
of news the headline and the news content were extracted.
### Source Data
#### Initial Data Collection and Normalization
The dataset was has been collected from https://tldr.tech/newsletter.
In order to clean up the samples and to construct a dataset better suited for headline generation we have applied a
couple of normalization steps:
1. The headlines initially contain an estimated read time in parentheses; we stripped this information from the
headline.
2. Some headlines are just a technology/repo/software name. We filtered out such samples if this name was not mention in
the news content.
#### Who are the source language producers?
The people (or person) behind the https://tldr.tech/ newsletter.
### Annotations
#### Annotation process
Disclaimers: The dataset was generated from a daily newsletter. The author had no intention for those newsletters to be
used as such.
#### Who are the annotators?
The newsletters were written by the people behind *TLDR tech*.
### Personal and Sensitive Information
[Needs More Information]
## Considerations for Using the Data
### Social Impact of Dataset
[Needs More Information]
### Discussion of Biases
This dataset only contains tech news. A model trained on such a dataset might not be able to generalize to other domain.
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
The dataset was obtained by collecting newsletters from this website: https://tldr.tech/newsletter
### Contributions
Thanks to [@JulesBelveze](https://github.com/JulesBelveze) for adding this dataset. |