Datasets:

baconnier
/

deepsynth-en-xsum

	---
	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