
# 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.
Also, the newsletter contain different sections. We add this extra information to every piece of news.

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": "Cana Unveils Molecular Beverage Printer, a ‘Netflix for Drinks’ That Can Make Nearly Any Type of Beverage ",
  "content": "Cana has unveiled a drink machine that can synthesize almost any drink. The machine uses a cartridge that contains flavor compounds that can be combined to create the flavor of nearly any type of drink. It is about the size of a toaster and could potentially save people from throwing hundreds of containers away every month by allowing people to create whatever drinks they want at home. Around $30 million was spent building Cana’s proprietary hardware platform and chemistry system. Cana plans to start full production of the device and will release pricing by the end of February.",
  "category": "Science and Futuristic Technology"
}
```

### Data Fields

- `headline (str)`: the piece of news' headline
- `content (str)`: the piece of news
- `category (str)`: newsletter section

### 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 news are sponsored and thus do not belong to any newsletter section. We create an additional category "Sponsor"
   for such samples.

#### 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.
task_categories:summarization task_categories:text2text-generation task_categories:text-generation task_ids:news-articles-headline-generation task_ids:text-simplification task_ids:language-modeling annotations_creators:other language_creators:other multilinguality:monolingual size_categories:1K<n<10K source_datasets:original language:en region:us
{"annotations_creators": ["other"], "language_creators": ["other"], "language": ["en"], "multilinguality": ["monolingual"], "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"], "pretty_name": "tldr_news"}
TAGS
#task_categories-summarization #task_categories-text2text-generation #task_categories-text-generation #task_ids-news-articles-headline-generation #task_ids-text-simplification #task_ids-language-modeling #annotations_creators-other #language_creators-other #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #region-us 


# Dataset Card for 'tldr_news'

## Table of Contents

- Dataset Description
    - Dataset Summary
    - Supported Tasks
    - Languages
- Dataset Structure
    - Data Instances
    - Data Fields
    - Data Splits
- Dataset Creation
    - Curation Rationale
    - Source Data
    - Annotations
    - Personal and Sensitive Information
- Considerations for Using the Data
    - Social Impact of Dataset
    - Discussion of Biases
    - Other Known Limitations
- Additional Information
    - Dataset Curators
    - Licensing Information
    - Citation Information

## Dataset Description

- Homepage: URL

### Dataset Summary

The 'tldr_news' dataset was constructed by collecting a daily tech newsletter (available
here). Then, for every piece of news, the 'headline' and its corresponding '
content' were extracted.
Also, the newsletter contain different sections. We add this extra information to every piece of news.

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:



### Data Fields

- 'headline (str)': the piece of news' headline
- 'content (str)': the piece of news
- 'category (str)': newsletter section

### Data Splits

- 'all': all existing daily newsletters available here.

## Dataset Creation

### Curation Rationale

This dataset was obtained by scrapping the collecting all the existing newsletter
available here.

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 URL

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 news are sponsored and thus do not belong to any newsletter section. We create an additional category "Sponsor"
   for such samples.

#### Who are the source language producers?

The people (or person) behind the URL 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



## Considerations for Using the Data

### Social Impact of Dataset



### 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



## Additional Information

### Dataset Curators

The dataset was obtained by collecting newsletters from this website: URL

### Contributions

Thanks to @JulesBelveze for adding this dataset.
# Dataset Card for 'tldr_news' ## Table of Contents

- Dataset Description
    - Dataset Summary
    - Supported Tasks
    - Languages
- Dataset Structure
    - Data Instances
    - Data Fields
    - Data Splits
- Dataset Creation
    - Curation Rationale
    - Source Data
    - Annotations
    - Personal and Sensitive Information
- Considerations for Using the Data
    - Social Impact of Dataset
    - Discussion of Biases
    - Other Known Limitations
- Additional Information
    - Dataset Curators
    - Licensing Information
    - Citation Information ## Dataset Description

- Homepage: URL ### Dataset Summary

The 'tldr_news' dataset was constructed by collecting a daily tech newsletter (available
here). Then, for every piece of news, the 'headline' and its corresponding '
content' were extracted.
Also, the newsletter contain different sections. We add this extra information to every piece of news.

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: ### Data Fields

- 'headline (str)': the piece of news' headline
- 'content (str)': the piece of news
- 'category (str)': newsletter section ### Data Splits

- 'all': all existing daily newsletters available here. ## Dataset Creation ### Curation Rationale

This dataset was obtained by scrapping the collecting all the existing newsletter
available here.

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 URL

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 news are sponsored and thus do not belong to any newsletter section. We create an additional category "Sponsor"
   for such samples. #### Who are the source language producers?

The people (or person) behind the URL 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 ## Considerations for Using the Data ### Social Impact of Dataset ### 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 ## Additional Information ### Dataset Curators

The dataset was obtained by collecting newsletters from this website: URL ### Contributions

Thanks to @JulesBelveze for adding this dataset.
TAGS
#task_categories-summarization #task_categories-text2text-generation #task_categories-text-generation #task_ids-news-articles-headline-generation #task_ids-text-simplification #task_ids-language-modeling #annotations_creators-other #language_creators-other #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #region-us 
 # Dataset Card for 'tldr_news' ## Table of Contents

- Dataset Description
    - Dataset Summary
    - Supported Tasks
    - Languages
- Dataset Structure
    - Data Instances
    - Data Fields
    - Data Splits
- Dataset Creation
    - Curation Rationale
    - Source Data
    - Annotations
    - Personal and Sensitive Information
- Considerations for Using the Data
    - Social Impact of Dataset
    - Discussion of Biases
    - Other Known Limitations
- Additional Information
    - Dataset Curators
    - Licensing Information
    - Citation Information ## Dataset Description

- Homepage: URL ### Dataset Summary

The 'tldr_news' dataset was constructed by collecting a daily tech newsletter (available
here). Then, for every piece of news, the 'headline' and its corresponding '
content' were extracted.
Also, the newsletter contain different sections. We add this extra information to every piece of news.

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: ### Data Fields

- 'headline (str)': the piece of news' headline
- 'content (str)': the piece of news
- 'category (str)': newsletter section ### Data Splits

- 'all': all existing daily newsletters available here. ## Dataset Creation ### Curation Rationale

This dataset was obtained by scrapping the collecting all the existing newsletter
available here.

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 URL

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 news are sponsored and thus do not belong to any newsletter section. We create an additional category "Sponsor"
   for such samples. #### Who are the source language producers?

The people (or person) behind the URL 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 ## Considerations for Using the Data ### Social Impact of Dataset ### 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 ## Additional Information ### Dataset Curators

The dataset was obtained by collecting newsletters from this website: URL ### Contributions

Thanks to @JulesBelveze for adding this dataset.