task_categories:summarization size_categories:100K<n<1M language:ar license:cc-by-nc-4.0 chemistry biology legal finance music art code climate medical region:us
---
license: cc-by-nc-4.0
task_categories:
- summarization
language:
- ar
tags:
- chemistry
- biology
- legal
- finance
- music
- art
- code
- climate
- medical
pretty_name: AGS Corpus
size_categories:
- 100K<n<1M
---
# Dataset Card for AGS

## Table of Contents
- Dataset Description
  - Dataset Summary
  - Supported Tasks and Leaderboards
  - Languages
- Dataset Structure
  - Data Instances
  - Data Fields
- Dataset Creation
  - Curation Rationale
  - Source Data
  - Personal and Sensitive Information
 

## Dataset Description

- **Paper:** [Atef, A., Seddik, F., & Elbedewy, A. (2023).
AGS: Arabic GPT Summarization Corpus]()
- **Point of Contact:** fahdseddik@gmail.com

### Dataset Summary

AGS is the first publicly accessible abstractive summarization dataset for Arabic. It consists of 142,000 pairs of articles and summaries, all written in Modern Standard Arabic (MSA). The summaries are generated using GPT-3.5 Turbo, a large language model, through meticulous prompt engineering. The dataset covers a wide range of topics, such as politics, sports, culture, science, and technology.

### Supported Tasks and Leaderboards

The supported task is abstractive text summarization, which involves generating a concise and informative summary from a longer text. The dataset can be used to train and evaluate models for this task, as well as to benchmark their performance against existing methods.

There is no official leaderboard for this dataset, but the we report the results of several models on the test set, using Rouge-L, SS-Population mean, and Compression ratio metrics. The best performing model is mT5, which achieves 21.27, 82.65, and 62 scores on these metrics, respectively.

### Languages

The dataset is in Arabic (ISO 639-1: ar).

## Dataset Structure

### Data Instances

An example data instance is:
```
{ “text”: “نظرية التعقيد هي فرع من فروع نظرية
 الحوسبة والرياضيات، وهذه النظرية تتركز في تصنيف المسائل الحاسوبية حسب صعوبتها وربط أقسام التعقيد ببعضها، والمسألة
 الحاسوبية هي المسألة التي يستطيع الحاسوب بحلها.ويمكن اعتبارها مسألة صعبة إذا استخدمت كمية مُعينة من الموارد أياً كانت
 الخوارزمية. ولعل النماذج الحسابية هي الطريقة الأمثل في هذه النظرية لدراسة هذه المسائل وتحديد كمية الموارد اللازمة
 مثل: الوقت أو حجم المكان الإضافي اللازم، وتوجد معايير تعقيد أخرى مثل: الاتصال (مستخدم في نظرية تعقيد الاتصال) وعدد
 البوابات في الدارات المنطقية (مستخدم في نظرية تعقيد الدارات المنطقية) وكذلك عدد المعالجات (مستخدم في الحساب المتوازي).”,
 “summary”: “نظرية التعقيد هي فرع من نظرية
الحوسبة والرياضيات، تصنف المسائل الحاسوبية حسب صعوبتها وتربط أقسام التعقيد ببعضها. تحديد كمية الموارد
اللازمة يتم باستخدام النماذج الحسابية، مثل الوقت وحجم المكان الإضافي وعدد البوابات في الدارات المنطقية.” }
```

### Data Fields

- 'id' : an identification number
- `text`: the original text of the article, written in Arabic.
- `summary`: the abstractive summary of the article, written in Arabic.


## Dataset Creation

### Curation Rationale

The dataset was created to address the lack of abstractive summarization datasets for Arabic, which is a low-resource and under-studied language. The dataset aims to provide a large and diverse corpus of articles and summaries that can be used to train and evaluate models for this task, as well as to advance the research in this field.

### Source Data

The source data was collected from Wikipedia & Youm7 websites, covering a wide range of topics, such as politics, sports, culture, science, and technology. The websites were selected based on their popularity, credibility, and content quality. The data collection process involved web crawling, text sampling, and prompt engineering.

### Personal and Sensitive Information

The dataset does not contain any personal or sensitive information, as it only consists of articles and summaries that are publicly available on the web. The dataset creators are not responsible for any misuse or harm that may result from the use of this data.

