---
license: "cc-by-4.0"
language:
  - ar
  - zh
  - cs
  - en
  - fr
  - el
  - he
  - hi
  - ro
  - es
task_categories:
  - summarization
tags:
  - multi-document
  - single-document
  - abstractive
  - multilingual
configs:
  - config_name: single
    description: "Single-document summarisation pairs (article + summary)."
  - config_name: multi
    description: "Multi-document summarisation clusters with 3 human summaries."
---


# 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.