---
language:
  - en
  - ja
  - ko
  - si
  - ta
pretty_name: M2DS
tags:
  - multilingual summarisation
  - multi-document summarisation
  - dataset
  - nlp
  - bbc
task_categories:
  - summarization
size_categories:
  - 10K<n<100K
configs:
  - config_name: english
    default: true
    data_files:
      - split: train
        path: english/train.json
      - split: validation
        path: english/validation.json
      - split: test
        path: english/test.json
  - config_name: japanese
    data_files:
      - split: train
        path: japanese/train.json
      - split: validation
        path: japanese/validation.json
      - split: test
        path: japanese/test.json
  - config_name: korean
    data_files:
      - split: train
        path: korean/train.json
      - split: validation
        path: korean/validation.json
      - split: test
        path: korean/test.json
  - config_name: sinhala
    data_files:
      - split: train
        path: sinhala/train.json
      - split: validation
        path: sinhala/validation.json
      - split: test
        path: sinhala/test.json
  - config_name: tamil
    data_files:
      - split: train
        path: tamil/train.json
      - split: validation
        path: tamil/validation.json
      - split: test
        path: tamil/test.json
---

# M2DS v1.0 — Multilingual Dataset for Multi-document Summarisation

M2DS is a multilingual multi-document summarisation dataset built from BBC news articles and
professionally written BBC summaries across five languages: English, Japanese, Korean, Sinhala,
and Tamil.

## Quick start

```python
from datasets import load_dataset

# Load a specific language
ds = load_dataset("KushanH/m2ds", "english")

# Access splits
train = ds["train"]
val   = ds["validation"]
test  = ds["test"]

# Inspect a single example
print(train[0]["document"])  # concatenated source articles
print(train[0]["summary"])   # reference summary
```

Available config names: `english`, `japanese`, `korean`, `sinhala`, `tamil`.

## Dataset structure

Each language is released as split-based files compatible with Hugging Face `load_dataset()`.

### Splits

| Split      | Purpose                  |
|------------|--------------------------|
| `train`      | Model training           |
| `validation` | Hyperparameter tuning    |
| `test`       | Final evaluation         |

### Fields

Each row represents one **multi-document cluster** and contains two fields:

| Field      | Type   | Description                                                       |
|------------|--------|-------------------------------------------------------------------|
| `document` | string | Multiple related source articles concatenated into one text field |
| `summary`  | string | Reference summary combining BBC summaries for the cluster         |

### Document separator

Within the `document` field, individual articles are separated by:

```
|||||
```

Example:

```
Article one text here... ||||| Article two text here... ||||| Article three text here...
```

## Split ratios

- English: **80 / 10 / 10**
- Japanese, Korean, Sinhala, Tamil: **90 / 5 / 5**

## Statistics

| Language  |  Train | Validation |  Test |  Total |  Paper |
|-----------|-------:|-----------:|------:|-------:|-------:|
| English   | 13,496 |      1,688 | 1,687 | 16,871 |    17K |
| Japanese  |  9,891 |        549 |   551 | 10,991 |    11K |
| Korean    |  7,021 |        391 |   390 |  7,802 |     8K |
| Sinhala   |  4,942 |        275 |   275 |  5,492 |  5.5K  |
| Tamil     |  8,916 |        495 |   496 |  9,907 |    10K |
| **Total** | **44,266** | **3,398** | **3,399** | **51,063** | **~51.5K** |

Paper-reported values are rounded per-language presentation values.

## External resources

- OSF Archive: https://osf.io/7gjtm/
- GitHub Repository: https://github.com/KushanMH/m2ds

## Citation

If you use M2DS in your research, please cite:

```bibtex
@inproceedings{hewapathirana2024m2ds,
  title={M2DS: Multilingual Dataset for Multi-document Summarisation},
  author={Hewapathirana, Kushan and de Silva, Nisansa and Athuraliya, CD},
  booktitle={International Conference on Computational Collective Intelligence},
  pages={219--231},
  year={2024},
  organization={Springer}
}
```