task_categories:text2text-generation task_ids:news-articles-summarization annotations_creators:found language_creators:found multilinguality:multilingual size_categories:1M<n<10M source_datasets:original language:ca language:es license:odbl region:us
The Dataset for Automatic summarization of Catalan and Spanish newspaper
Articles (DACSA) corpus. It is a high-quality large-scale corpus that can be
used to train summarization models for Catalan and Spanish. The data provides
pairs of news article and its summary from different newspapers for both, the
Catalan and the Spanish languages. Regarding the Catalan set, there are 725,184
sample pairs from 9 newspapers, regarding the Spanish set, the corpus provides
2,120,649 sample pairs from 21 newspapers.
@inproceedings{segarra-soriano-etal-2022-dacsa,
    title = "{DACSA}: A large-scale Dataset for Automatic summarization of {C}atalan and {S}panish newspaper Articles",
    author = "Segarra Soriano, Encarnaci{\'o}n  and
      Ahuir, Vicent  and
      Hurtado, Llu{\'\i}s-F.  and
      Gonz{\'a}lez, Jos{\'e}",
    booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jul,
    year = "2022",
    address = "Seattle, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2022.naacl-main.434",
    doi = "10.18653/v1/2022.naacl-main.434",
    pages = "5931--5943",
    abstract = "The application of supervised methods to automatic summarization requires the availability of adequate corpora consisting of a set of document-summary pairs. As in most Natural Language Processing tasks, the great majority of available datasets for summarization are in English, making it difficult to develop automatic summarization models for other languages. Although Spanish is gradually forming part of some recent summarization corpora, it is not the same for minority languages such as Catalan.In this work, we describe the construction of a corpus of Catalan and Spanish newspapers, the Dataset for Automatic summarization of Catalan and Spanish newspaper Articles (DACSA) corpus. It is a high-quality large-scale corpus that can be used to train summarization models for Catalan and Spanish.We have carried out an analysis of the corpus, both in terms of the style of the summaries and the difficulty of the summarization task. In particular, we have used a set of well-known metrics in the summarization field in order to characterize the corpus. Additionally, for benchmarking purposes, we have evaluated the performances of some extractive and abstractive summarization systems on the DACSA corpus.",
}
---
task_categories:
- text2text-generation
task_ids:
- news-articles-summarization
language:
- ca
- es
size_categories:
- 1M<n<10M
license:
- odbl
multilinguality:
- multilingual
source_datasets:
- original
paperswithcode_id: dacsa
annotations_creators:
- found
language_creators:
- found
pretty_name: DACSA 
---

# Dataset Card for "DACSA"

## Table of Contents
- [Dataset Card Creation Guide](#dataset-card-creation-guide)
  - [Table of Contents](#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)
      - [Initial Data Collection and Normalization](#initial-data-collection-and-normalization)
      - [Who are the source language producers?](#who-are-the-source-language-producers)
    - [Annotations](#annotations)
      - [Annotation process](#annotation-process)
      - [Who are the annotators?](#who-are-the-annotators)
    - [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

- **Paper:** [DACSA: A large-scale Dataset for Automatic summarization of Catalan and Spanish newspaper Articles](https://aclanthology.org/2022.naacl-main.434/)
- **Point of Contact:** [Vicent Ahuir](mailto:viahes@dsic.upv.es)

### Dataset Summary

The Dataset for Automatic summarization of Catalan and Spanish newspaper
Articles (DACSA) corpus. It is a high-quality large-scale corpus that can be
used to train summarization models for Catalan and Spanish. The data provides
pairs of news article and its summary from different newspapers for both, the
Catalan and the Spanish languages. Regarding the Catalan set, there are 725,184
sample pairs from 9 newspapers, regarding the Spanish set, the corpus provides
2,120,649 sample pairs from 21 newspapers.


### Supported Tasks and Leaderboards

[More information needed](https://github.com/csebuetnlp/xl-sum)

### Languages

-  `catalan`
-  `spanish`

## Dataset Structure

### Data Fields
-  'id': A string representing the article ID.
-  'summary': A string containing the article summary.
-  'article' : A string containing the article text. 


### Data Splits

Four splits are provided for each language set
- **train**: samples for training models
- **validation**: samples for adjusting and validating models
- **test.i**: test samples from newspapers present in _train_ and _validation_ splits
- **test.ni**: test samples from newspapers not present in training and validation splits

The _validation_ and _test-i_ splits contain a uniform distribution of samples
from each newspaper source.

Languages     | ISO 639-1 Code | Train   | Val   | Test.i | Test.ni | Total   |
--------------|----------------|---------|-------|--------|---------|---------|
Catalan       | ca             | 636596  | 35376 | 35376  | 17836   | 725184  |
Spanish       | es             | 1802919 | 104052 | 104052 | 109626 | 2120649 |

## Dataset Creation

### Curation Rationale

[More information needed](https://github.com/csebuetnlp/xl-sum)

### Source Data

Newspapers from Spain that publish news in Catalan or Spanish

#### Initial Data Collection and Normalization

[Detailed in the paper](https://aclanthology.org/2022.naacl-main.434/) 

## Considerations for Using the Data

### Social Impact of Dataset

[More information needed](https://aclanthology.org/2022.naacl-main.434/) 

### Discussion of Biases

[More information needed](https://aclanthology.org/2022.naacl-main.434/) 

### Other Known Limitations

[More information needed](https://aclanthology.org/2022.naacl-main.434/) 

## Additional Information

### Dataset Curators

[More information needed](https://aclanthology.org/2022.naacl-main.434/) 

### Licensing Information

These data are released under this licensing scheme.
We do not own any of the text from which these data has been extracted.

This DACSA dataset package is made available under the Open Database License: http://opendatacommons.org/licenses/odbl/1.0/. Any rights in individual contents of the database are licensed under the Database Contents License: http://opendatacommons.org/licenses/dbcl/1.0/

Should you consider that our data contains material that is owned by you 
and should therefore not be reproduced here, please:
* Clearly identify yourself, with detailed contact data such as an address, 
telephone number or email address at which you can be contacted.
* Clearly identify the copyrighted work claimed to be infringed.
* Clearly identify the material that is claimed to be infringing and 
information reasonably sufficient to allow us to locate the material.
We will comply to legitimate requests by removing the affected sources 
from the next release of the corpus. 

### Citation Information

If you use any of the datasets, models or code modules, please cite the following paper:
```
@inproceedings{segarra-soriano-etal-2022-dacsa,
    title = "{DACSA}: A large-scale Dataset for Automatic summarization of {C}atalan and {S}panish newspaper Articles",
    author = "Segarra Soriano, Encarnaci{\'o}n  and
      Ahuir, Vicent  and
      Hurtado, Llu{\'\i}s-F.  and
      Gonz{\'a}lez, Jos{\'e}",
    booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jul,
    year = "2022",
    address = "Seattle, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2022.naacl-main.434",
    doi = "10.18653/v1/2022.naacl-main.434",
    pages = "5931--5943",
    abstract = "The application of supervised methods to automatic summarization requires the availability of adequate corpora consisting of a set of document-summary pairs. As in most Natural Language Processing tasks, the great majority of available datasets for summarization are in English, making it difficult to develop automatic summarization models for other languages. Although Spanish is gradually forming part of some recent summarization corpora, it is not the same for minority languages such as Catalan.In this work, we describe the construction of a corpus of Catalan and Spanish newspapers, the Dataset for Automatic summarization of Catalan and Spanish newspaper Articles (DACSA) corpus. It is a high-quality large-scale corpus that can be used to train summarization models for Catalan and Spanish.We have carried out an analysis of the corpus, both in terms of the style of the summaries and the difficulty of the summarization task. In particular, we have used a set of well-known metrics in the summarization field in order to characterize the corpus. Additionally, for benchmarking purposes, we have evaluated the performances of some extractive and abstractive summarization systems on the DACSA corpus.",
}
```
