AraFinNews: The Arabic Financial News Dataset (212K)

For the JSON file format please check our AraFinNews GitHub repo

AraFinNews is the largest openly available dataset of Arabic financial news, comprising 212,500 full-length articles collected from Argaam.com — a leading financial news portal in the Arab world.
The dataset provides structured, machine-readable text suitable for research in financial NLP, abstractive summarisation, event extraction, and domain-specific language modelling.

Comparable to the CNN/DailyMail dataset for English, AraFinNews offers an Arabic equivalent for headline-style abstractive summarisation. Each record pairs a full Arabic financial article with its professionally written headline, enabling high-quality training and evaluation of summarisation and financial text understanding systems.

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📰 Dataset Overview

Field	Description
id	Unique numeric identifier
title	Arabic headline (financial news title)
date	Publication date (ISO format)
article	Full article text
url	Public link on Argaam.com

• Total articles: 212,500
• Language: Modern Standard Arabic
• Domain: Finance, markets, economics, corporate activity
• Format: CSV (UTF-8)
• Licence: CC BY-NC 4.0

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⚙️ Updated Data Splits

In addition to the original ID lists, the repository now includes fully populated split files:

• AraFinNews_train.csv — 80% of the dataset
• AraFinNews_validation.csv — 10%
• AraFinNews_test.csv — 10%

These files contain the complete article and headline rows, making them directly compatible with the Hugging Face Dataset Viewer and eliminating the need to reconstruct splits manually.

The older ID-only files are still included for reference:

• AraFinNews_train_ids.csv
• AraFinNews_val_ids.csv
• AraFinNews_test_ids.csv

These can be used if a user prefers to work from the master file AraFinNews.csv.

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⚙️ Intended Use

AraFinNews supports research in:

• Abstractive and extractive summarisation
• Financial event and entity extraction
• Sentiment and stance analysis in financial narratives
• Domain-specific pretraining and adaptation of Arabic LLMs
• Financial question answering and narrative analysis

The dataset is released strictly for non-commercial research and educational use.

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📦 Access and Usage

Load the full dataset

import pandas as pd

df = pd.read_csv("AraFinNews.csv")
df.sample(5)

Load directly from the full splits

import pandas as pd

train = pd.read_csv("AraFinNews_train.csv")
val = pd.read_csv("AraFinNews_validation.csv")
test = pd.read_csv("AraFinNews_test.csv")

(Optional) Load splits using ID files

import pandas as pd

df = pd.read_csv("AraFinNews.csv")
train_ids = pd.read_csv("AraFinNews_train_ids.csv")["id"]
train_df = df[df["id"].isin(train_ids)]

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📚 Citation

If you use this dataset, please cite:

El-Haj, M. & Rayson, P. (2025). AraFinNews: Arabic Financial Summarisation with Domain-Adapted LLMs. Proceedings of IEEE Big Data 2025.

This work is associated with the following preprint:
https://arxiv.org/abs/2511.01265

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🏛️ Repository Structure

Note: json files are on AraFinNews GitHub

AraFinNews/
│
├── AraFinNews.csv
├── train.csv
├── validation.csv
├── test.csv
│
├── AraFinNews_train_ids.csv
├── AraFinNews_val_ids.csv
├── AraFinNews_test_ids.csv
│
├── AraFinNews_json_files/
│   ├── 000001.json
│   ├── 000002.json
│   └── ...
│
└── README.md

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Contact:
Mo El-Haj
VinUniversity, Hanoi / Lancaster University, UK
dr.melhaj@gmail.com