---
language:
- en
size_categories:
- 10K<n<100K
task_categories:
- summarization
- image-to-text
- text-generation
tags:
- summarization
- vision
- DeepSeek-OCR
- multilingual
- visual-text-encoding
- random-augmentation
library_name: datasets
license: mit
pretty_name: "XSum BBC News Summarization"
dataset_info:
  features:
  - name: text
    dtype: string
  - name: summary
    dtype: string
  - name: image
    dtype: image
  - name: source_dataset
    dtype: string
  - name: original_split
    dtype: string
  - name: original_index
    dtype: int64
  splits:
  - name: train
    num_examples: 204017
---

# DeepSynth - XSum BBC News Summarization

## Dataset Description

BBC news articles with single-sentence summaries. Focused on extreme summarization where the summary is
a single sentence capturing the essence of the article.

This dataset is part of the **DeepSynth** project, which uses visual text encoding for multilingual summarization with the DeepSeek-OCR vision-language model. Text documents are converted into images and processed through a frozen 380M parameter visual encoder, enabling 20x token compression while preserving document layout and structure.

### Key Features

- **Original High-Quality Images**: Full-resolution images stored once, augmented on-the-fly during training
- **Random Augmentation Pipeline**: Rotation, perspective, color jitter, and resize transforms for better generalization
- **Visual Text Encoding**: 20x compression ratio (1 visual token ≈ 20 text tokens)
- **Document Structure Preservation**: Layout and formatting maintained through image representation
- **Human-Written Summaries**: High-quality reference summaries for each document
- **Deduplication Tracking**: Source dataset and index tracking prevents duplicates

### Dataset Statistics

- **Total Samples**: ~50,000
- **Language(s)**: English
- **Domain**: BBC news articles
- **Average Document Length**: ~400 tokens
- **Average Summary Length**: ~20 tokens (single sentence)

### Source Dataset

Based on the **XSum dataset** from BBC articles (2010-2017).
- **Original Authors**: Narayan et al. (2018)
- **Paper**: [Don't Give Me the Details, Just the Summary!](https://arxiv.org/abs/1808.08745)
- **License**: MIT License

## Image Augmentation Pipeline

Images are stored at **original resolution** (up to 1600×2200) and augmented during training for better generalization:

### Available Augmentation Transforms

- **Random Rotation**: ±10° rotation for orientation invariance
- **Random Perspective**: 0.1-0.2 distortion to simulate viewing angles
- **Random Resize**: 512-1600px range for multi-scale learning
- **Color Jitter**: Brightness, contrast, saturation adjustments (±20%)
- **Random Horizontal Flip**: Optional (use with caution for text)

All transforms preserve aspect ratio with padding to maintain text readability. This approach:
- **Reduces storage**: 6x less disk space (single image vs 6 resolutions)
- **Increases flexibility**: Any resolution on-the-fly vs pre-computed fixed sizes
- **Improves generalization**: Random transforms prevent overfitting to specific resolutions

## Dataset Structure

### Data Fields

- `text` (string): Original document text
- `summary` (string): Human-written summary
- `image` (PIL.Image): Original full-size rendered document image (up to 1600×2200)
- `source_dataset` (string): Origin dataset name
- `original_split` (string): Source split (train/validation/test)
- `original_index` (int): Original sample index for deduplication

### Data Example

```python
{
    'text': 'The government has announced new measures to...',
    'summary': 'Government unveils climate change plan.',
    'image': <PIL.Image>,  # Original resolution (up to 1600×2200)
    'source_dataset': 'Rexhaif/xsum_reduced',
    'original_split': 'train',
    'original_index': 0
}
```

## Usage

### Loading the Dataset

```python
from datasets import load_dataset

# Load full dataset
dataset = load_dataset("baconnier/deepsynth-en-xsum")

# Streaming for large datasets
dataset = load_dataset("baconnier/deepsynth-en-xsum", streaming=True)
```

### Training Example with DeepSeek-OCR and Augmentation

```python
from transformers import AutoProcessor, AutoModelForVision2Seq
from datasets import load_dataset
from deepsynth.data.transforms import create_training_transform

# Load model and processor
model = AutoModelForVision2Seq.from_pretrained("deepseek-ai/DeepSeek-OCR")
processor = AutoProcessor.from_pretrained("deepseek-ai/DeepSeek-OCR")

# Load dataset
dataset = load_dataset("baconnier/deepsynth-en-xsum")

# Create augmentation pipeline (random rotation, perspective, resize, color jitter)
transform = create_training_transform(
    target_size_range=(512, 1600),  # Random resize range
    rotation_degrees=10,             # ±10° rotation
    perspective_distortion=0.1,      # Perspective transform
    brightness_factor=0.2,           # ±20% brightness
    contrast_factor=0.2,             # ±20% contrast
)

# Process sample with augmentation
sample = dataset['train'][0]
augmented_image = transform(sample['image'])  # Apply random transforms
inputs = processor(
    images=augmented_image,
    text=sample['text'],
    return_tensors="pt"
)

# Fine-tune decoder only (freeze encoder)
for param in model.encoder.parameters():
    param.requires_grad = False

# Training loop with on-the-fly augmentation...
```

## Training Recommendations

### DeepSeek-OCR Fine-Tuning

```python
# Recommended hyperparameters with augmentation
training_args = {
    "learning_rate": 2e-5,
    "batch_size": 4,
    "gradient_accumulation_steps": 4,
    "num_epochs": 3,
    "mixed_precision": "bf16",
    "freeze_encoder": True,  # IMPORTANT: Only fine-tune decoder

    # Augmentation parameters
    "rotation_degrees": 10,           # Random rotation ±10°
    "perspective_distortion": 0.1,    # Perspective transform
    "resize_range": (512, 1600),      # Random resize 512-1600px
    "brightness_factor": 0.2,         # ±20% brightness
    "contrast_factor": 0.2,           # ±20% contrast
}
```

### Expected Performance

- **Baseline (text-to-text)**: ROUGE-1 ~40-42
- **DeepSeek-OCR (visual)**: ROUGE-1 ~44-47 (typical SOTA)
- **Training Time**: ~6-8 hours on A100 (80GB) for full dataset
- **GPU Memory**: ~40GB with batch_size=4, mixed_precision=bf16

## Dataset Creation

This dataset was created using the **DeepSynth** pipeline:

1. **Source Loading**: Original text documents from Rexhaif/xsum_reduced
2. **Text-to-Image Conversion**: Documents rendered as PNG images (DejaVu Sans 12pt, Unicode support)
3. **Original Resolution Storage**: Full-quality images stored once (up to 1600×2200)
4. **Incremental Upload**: Batches of 5,000 samples uploaded to HuggingFace Hub
5. **Deduplication**: Source tracking prevents duplicate samples

**Note**: Images are augmented on-the-fly during training using random transformations (rotation, perspective, resize, color jitter) for better generalization across different resolutions and conditions.

### Rendering Specifications

- **Font**: DejaVu Sans 12pt (full Unicode support for multilingual text)
- **Line Wrapping**: 100 characters per line
- **Margin**: 40px
- **Background**: White (255, 255, 255)
- **Text Color**: Black (0, 0, 0)
- **Format**: PNG with lossless compression

## Citation

If you use this dataset in your research, please cite:

```bibtex
@misc{deepsynth-en-xsum,
    title={{DeepSynth XSum BBC News Summarization: Visual Text Encoding with Random Augmentation for Summarization}},
    author={Baconnier},
    year={2025},
    publisher={HuggingFace},
    howpublished={\url{https://huggingface.co/datasets/baconnier/deepsynth-en-xsum}}
}
```

### Source Dataset Citation

```bibtex
@inproceedings{narayan2018don,
    title={Don't Give Me the Details, Just the Summary! Topic-Aware Convolutional Neural Networks for Extreme Summarization},
    author={Narayan, Shashi and Cohen, Shay B and Lapata, Mirella},
    booktitle={Proceedings of EMNLP},
    year={2018}
}
```

## License

MIT License - See source dataset for full license terms.

**Note**: This dataset inherits the license from the original source dataset. Please review the source license before commercial use.

## Limitations and Bias

- **Extreme summarization**: Single-sentence summaries may lose important details
- **UK-centric**: Primarily British news and perspectives
- **Short summaries**: Not suitable for multi-sentence summary training
- **Temporal bias**: Articles from 2010-2017

## Additional Information

### Dataset Curators

Created by the DeepSynth team as part of multilingual visual summarization research.

### Contact

- **Repository**: [DeepSynth GitHub](https://github.com/bacoco/DeepSynth)
- **Issues**: [GitHub Issues](https://github.com/bacoco/DeepSynth/issues)

### Acknowledgments

- **DeepSeek-OCR**: Visual encoder from DeepSeek AI
- **Source Dataset**: Rexhaif/xsum_reduced
- **HuggingFace**: Dataset hosting and infrastructure