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README.md

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 ---
+language:
+  - ar
+  - zh
+  - cs
+  - en
+  - fr
+  - el
+  - he
+  - hi
+  - ro
+  - es
+
+pretty_name: "MultiLing Multilingual Summarisation Corpus (Single- and Multi-Document)"
+tags:
+  - summarisation
+  - multi-document
+  - multilingual
+  - abstractive
+  - news
+  - evaluation
+task_categories:
+  - summarization
 license: cc-by-4.0
+size_categories:
+  - 1K<n<10K
+dataset_info:
+  features:
+    - name: language
+      dtype: string
+    - name: doc_id
+      dtype: string
+    - name: documents_text
+      dtype: string
+    - name: summary
+      dtype: string
+    - name: summary_1
+      dtype: string
+    - name: summary_2
+      dtype: string
+    - name: summary_3
+      dtype: string
+    - name: doc_ids
+      dtype: string
+  splits:
+    - train
+    - validation
+    - test
+  task_templates:
+    - text2text-generation
 ---
+
+# MultiLing Multilingual Summarisation Corpus
+
+The **MultiLing Multilingual Summarisation Corpus** is a comprehensive multilingual benchmark for **single-document** and **multi-document abstractive summarisation**, originally created for the *MultiLing 2011* and *MultiLing 2013* shared tasks held under ACL.  
+This release consolidates, cleans, and reformats the original resources into a standard, machine-readable dataset suitable for modern sequence-to-sequence and large language model research.
+
+The corpus covers **ten languages**:
+
+**Arabic, Chinese, Czech, English, French, Greek, Hebrew, Hindi, Romanian, Spanish**
+
+and contains both:
+
+- **Single-document summarisation pairs** (source article, 1 gold summary)
+- **Multi-document summarisation clusters** (10 related articles, 3 human-written abstractive summaries)
+
+All texts originate from **WikiNews**, following the Creative Commons BY licence. See http://multiling.iit.demokritos.gr/
+
+---
+
+## ✨ Key Features
+
+### **Single-Document Summarisation**
+- 40 languages in the original collection; the present release includes the main ten used for MultiLing 2013.
+- For each language, every document has:
+  - One source article  
+  - One human abstractive summary  
+- Fully parallel across languages.
+
+### **Multi-Document Summarisation**
+- Each topic consists of **10 articles describing an event sequence**.
+- Topics appear consistently across all languages that contributed to that year’s task.
+- Each cluster includes **three human-written abstractive summaries**, produced independently.
+- Human summaries were constrained to **240–250 words** (or equivalent byte limits for Chinese).
+
+### **Parallel & Comparable Structure**
+The corpus was originally designed to allow:
+- Cross-lingual and multilingual summarisation  
+- Comparative analyses of summarisation difficulty across languages  
+- Multilingual evaluation of automatic summarisation metrics (ROUGE, AutoSummENG-MeMoG, NPowER)
+
+---
+
+## 📘 Source and Citation
+
+This dataset is derived from the corpus described in:
+
+**Li L, Forăscu C, El-Haj M, Giannakopoulos G. (2013)**  
+*Multi-Document Multilingual Summarization Corpus Preparation, Part 1: Arabic, English, Greek, Chinese, Romanian.*  
+In **Proceedings of the MultiLing 2013 Workshop on Multilingual Multi-Document Summarization**, pp. 1–12.  
+ACL 2013, Sofia, Bulgaria.  
+PDF: https://aclanthology.org/W13-3101.pdf
+
+Please cite the paper above when using this dataset.
+
+---
+
+## 🗂 Dataset Structure
+
+### **Single-Document Format**
+Each sample includes:
+
+- `language` — ISO folder name (`ar`, `en`, `fr`, etc.)
+- `doc_id`
+- `document_text`
+- `summary`
+
+### **Multi-Document Format**
+Each sample includes:
+
+- `cluster_id` — e.g. `M000`, `M014`, `M103`
+- `language`  
+- `doc_ids` — list of the ten document identifiers  
+- `documents_text` — concatenated with `<DOC id=…>` wrappers  
+- `summary_1`, `summary_2`, `summary_3` — three reference human summaries
+
+All files are provided in **CSV** and **JSONL**, with **train/dev/test** splits.
+
+---
+
+## 🔧 Recommended Use Cases
+
+- Multilingual abstractive summarisation  
+- Cross-lingual evaluation of LLMs  
+- Multi-document summarisation research  
+- Training summarisation models on parallel news texts  
+- Research on multilingual evaluation metrics  
+- Cross-lingual transfer learning  
+- Low-resource summarisation investigations  
+
+---
+
+## 📊 Splits
+
+The dataset is released with deterministic:
+
+- `train`
+- `validation`
+- `test`
+
+splits for **single-document** and **multi-document** subsets.  
+For multi-document summarisation, splits are **cluster-based** to prevent data leakage.
+
+---
+
+## 📥 Loading the Dataset
+
+```python
+from datasets import load_dataset
+
+ds = load_dataset("YOUR_DATASET_NAME", "multi")
+# or
+ds = load_dataset("YOUR_DATASET_NAME", "single")
+
+
+---
+
+## 🔒 Licence
+
+All texts originate from WikiNews under Creative Commons BY 2.5/3.0 licences.
+This consolidated dataset is released under CC-BY-4.0.
+
+---
+
+## 🙏 Acknowledgements
+
+MultiLing is the result of a large international community effort involving contributors from more than ten universities and research centres.
+This cleaned and repackaged release builds on that original work to make the corpus more accessible for modern NLP research.

single_docs.csv

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single_docs.jsonl

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single_docs_train.csv

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