README.md

---
license: cc-by-4.0
task_categories:
  - summarization
language:
  - ar
size_categories:
  - 1K<n<10K
pretty_name: "EASC: The Essex Arabic Summaries Corpus"

dataset_info:
  features:
    - name: article_id
      dtype: int32
    - name: topic_name
      dtype: string
    - name: article_text
      dtype: string
    - name: summary_A
      dtype: string
    - name: summary_B
      dtype: string
    - name: summary_C
      dtype: string
    - name: summary_D
      dtype: string
    - name: summary_E
      dtype: string

  splits:
    - name: train
    - name: validation
    - name: test
---


# EASC: The Essex Arabic Summaries Corpus

Mo El-Haj, Udo Kruschwitz, Chris Fox  
University of Essex, UK  

This repository hosts **EASC** — the Essex Arabic Summaries Corpus — a collection of **153 Arabic source documents** and **765 human-generated extractive summaries**, created using Amazon Mechanical Turk.

EASC is one of the earliest publicly available datasets for **Arabic single-document summarisation** and remains widely used in research on Arabic NLP, extractive summarisation, sentence ranking, and evaluation.

---

## 📘 Background

EASC was introduced in:

**El-Haj, M., Kruschwitz, U., & Fox, C. (2010).  
*Using Mechanical Turk to Create a Corpus of Arabic Summaries.*  
Workshop on LRs & HLT for Semitic Languages @ LREC 2010.**

The corpus was motivated by the lack of gold-standard resources for evaluating **Arabic text summarisation**, particularly extractive systems. Mechanical Turk was used to collect **five independent extractive summaries per article**, offering natural diversity and enabling aggregation into different gold-standard levels.

The work was later expanded in:

- **El-Haj (2012). *Multi-document Arabic Text Summarisation.* PhD Thesis, University of Essex.**  
- **El-Haj, Kruschwitz & Fox (2011). Exploring clustering for multi-document Arabic summarisation. AIRS 2011.**

---

## 🗂 Corpus Contents

EASC contains:

| Component | Count | Description |
|----------|-------|-------------|
| Articles | 153 | Arabic Wikipedia + AlRai (Jordan) + AlWatan (Saudi Arabia) |
| Summaries | 765 | Five extractive summaries per article |
| Topics | 10 | Art, Environment, Politics, Sport, Health, Finance, Science & Technology, Tourism, Religion, Education |

Each summary was produced by a different Mechanical Turk worker, who selected up to **50% of the sentences** they considered most important.

---

## 📁 Directory Structure

```

Articles/
  Article001/
  Article002/
  ...
MTurk/
  Article001/
  Article002/
  ...
  
```

Where:


- `Articles/ArticleXX/*.txt` → full document  

- `MTurk/ArticleXX/Dxxxx.M.250.A.#.*` → five extractive summaries (A–E)

---



## 📦 Modern Dataset Format (this repository)



To make EASC easier to use with modern NLP tools, this repository includes a **unified CSV/JSONL version**:



### **CSV Schema**


| Field         | Description                         |
|---------------|-------------------------------------|
| `article_id`  | Unique article identifier (1–153)   |
| `topic_name`  | Topic label extracted from filename |
| `article_text`| Full article text                   |
| `summary_A`   | Human summary A                     |
| `summary_B`   | Human summary B                     |
| `summary_C`   | Human summary C                     |
| `summary_D`   | Human summary D                     |
| `summary_E`   | Human summary E                     |

### **JSON Schema**
One JSON Object per article:

{
  "article_id": 1,
  "topic_name": "Art and Music",
  "article_text": "...",
  "summaries": ["...", "...", "...", "...", "..."]
}

---

## 🛠️ Regenerating the CSV / JSONL

The following Python script reconstructs the unified dataset from the raw Articles/ and MTurk/ folders (strict UTF-8):

```
import os
import re
import json
import pandas as pd

ARTICLES_DIR = "Articles"
MTURK_DIR = "MTurk"

records_csv = []
records_jsonl = []

for folder in sorted(os.listdir(ARTICLES_DIR)):
    folder_path = os.path.join(ARTICLES_DIR, folder)
    if not os.path.isdir(folder_path):
        continue

    m = re.match(r"Article(\d+)", folder)
    if not m:
        continue

    article_id = int(m.group(1))
    article_files = os.listdir(folder_path)

    article_file = article_files[0]
    article_file_path = os.path.join(folder_path, article_file)

    base = os.path.splitext(article_file)[0]
    match = re.match(r"(.+?)\s*\(\d+\)", base)
    topic_name = match.group(1).strip() if match else "Unknown"

    with open(article_file_path, "r", encoding="utf-8", errors="replace") as f:
        article_text = f.read().strip()

    summaries_dir = os.path.join(MTURK_DIR, folder)
    summary_files = sorted(os.listdir(summaries_dir))
    summaries = []

    for sfile in summary_files:
        s_path = os.path.join(summaries_dir, sfile)
        with open(s_path, "r", encoding="utf-8", errors="replace") as f:
            summaries.append(f.read().strip())

    while len(summaries) < 5:
        summaries.append("")
    summaries = summaries[:5]

    records_csv.append({
        "article_id": article_id,
        "topic_name": topic_name,
        "article_text": article_text,
        "summary_A": summaries[0],
        "summary_B": summaries[1],
        "summary_C": summaries[2],
        "summary_D": summaries[3],
        "summary_E": summaries[4]
    })

    records_jsonl.append({
        "article_id": article_id,
        "topic_name": topic_name,
        "article_text": article_text,
        "summaries": summaries
    })

df = pd.DataFrame(records_csv)
df.to_csv("EASC.csv", index=False, encoding="utf-8")

with open("EASC.jsonl", "w", encoding="utf-8") as f:
    for row in records_jsonl:
        f.write(json.dumps(row, ensure_ascii=False) + "\n")

print("Done! Created EASC.csv and EASC.jsonl")


```
---

## 📥 Train / Validation / Test Splits
```
import pandas as pd
from sklearn.model_selection import train_test_split

df = pd.read_csv("EASC.csv")

train_df, temp_df = train_test_split(df, test_size=0.2, random_state=42)
val_df, test_df = train_test_split(temp_df, test_size=0.5, random_state=42)

train_df.to_csv("EASC_train.csv", index=False)
val_df.to_csv("EASC_val.csv", index=False)
test_df.to_csv("EASC_test.csv", index=False)

```
---

## 🎯 Intended Use

EASC supports research in:

- Extractive summarisation  

- Sentence ranking and scoring  

- Gold-summary aggregation (Level2, Level3)  

- ROUGE and Dice evaluation  

- Learning sentence importance  

- Human–machine evaluation comparisons  

- Crowdsourcing quality analysis


EASC is the only Arabic summarisation dataset with:

- consistent multiple references per document  

- real extractive human judgements  

- cross-worker variability suitable for probabilistic modelling
---



## 📊 Recommended Gold Standards

Based on the original paper:

- **Level 3**: sentences selected by ≥3 workers  

- **Level 2**: sentences selected by ≥2 workers  

- **All**: all sentences selected by any worker  

&nbsp; (not recommended as a gold standard; used for analysis only)



These levels can be regenerated programmatically from the unified CSV.



---



## 🧪 Evaluations (from the 2010 paper)


Systems evaluated against EASC include:

- Sakhr Arabic Summariser  

- AQBTSS  

- Gen-Summ  

- LSA-Summ  

- Baseline-1 (first sentence)  



Metrics used:

- **Dice coefficient** (recommended for extractive summarisation)  

- **ROUGE-2 / ROUGE-L / ROUGE-W / ROUGE-S**  

- **AutoSummENG**



All details are documented in the LREC 2010 paper.

---

## 📑 Citation



If you use EASC, please cite:



El-Haj, M., Kruschwitz, U., & Fox, C. (2010).  

Using Mechanical Turk to Create a Corpus of Arabic Summaries.  

In LRs & HLT for Semitic Languages Workshop, LREC 2010.



Additional references:



El-Haj (2012). *Multi-document Arabic Text Summarisation.* PhD Thesis.  


El-Haj, Kruschwitz & Fox (2011). *Exploring Clustering for Multi-Document Arabic Summarisation.* AIRS 2011.



## 📜 Licence

The original EASC release permits research use.  

This cleaned and reformatted version follows the same academic-research usage terms.



## ✔ Notes


- Some Mechanical Turk summaries may include noisy selections or inconsistent behaviour; these are preserved to avoid subjective filtering.
- File encodings reflect the original dataset; all modern versions are normalised to UTF-8.
- The unified CSV/JSONL is provided for convenience and reproducibility.



## 🧭 Maintainer



Dr Mo El-Haj  

Associate Professor in Natural Language Processing  

VinUniversity, Vietnam / Lancaster University, UK