Datasets:

drelhaj
/

EASC

@@ -1,342 +1,375 @@
-# 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

+---
+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
+      type: int32
+    - name: topic_name
+      type: string
+    - name: article_text
+      type: string
+    - name: summary_A
+      type: string
+    - name: summary_B
+      type: string
+    - name: summary_C
+      type: string
+    - name: summary_D
+      type: string
+    - name: summary_E
+      type: 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