Datasets:

sumuks
/

openreview-pdfs

	---
	license: mit
	task_categories:
	- text-classification
	- document-question-answering
	language:
	- en
	tags:
	- academic-papers
	- openreview
	- research
	- pdf
	- machine-learning
	size_categories:
	- 1K<n<10K
	---

	# OpenReview PDFs Dataset

	## 🎯 Overview
	This dataset contains 7,814 PDF files from OpenReview, representing a comprehensive collection of machine learning and AI research papers. The papers are organized in clean subdirectories for efficient access and processing.

	## 📁 Repository Structure
	```
	data/
	├── small_papers/ # 1,872 files (< 500KB each)
	├── medium_papers/ # 4,605 files (500KB - 5MB each)
	└── large_papers/ # 1,337 files (>= 5MB each)
	```

	## 🚀 Quick Start

	### Installation
	```bash
	pip install datasets pdfplumber
	```

	### Basic Usage
	```python
	from datasets import load_dataset

	# Load the dataset
	dataset = load_dataset("sumuks/openreview-pdfs")

	# Access a PDF
	sample = dataset['train'][0]
	pdf_obj = sample['pdf']

	# Extract text from first page
	if pdf_obj.pages:
	text = pdf_obj.pages[0].extract_text()
	print(text[:500]) # First 500 characters
	```

	### Advanced Usage
	```python
	import pandas as pd

	# Extract metadata from all PDFs
	pdf_data = []

	for i, sample in enumerate(dataset['train']):
	pdf_obj = sample['pdf']

	# Get metadata
	title = "Unknown"
	author = "Unknown"
	if hasattr(pdf_obj, 'metadata') and pdf_obj.metadata:
	title = pdf_obj.metadata.get('Title', 'Unknown')
	author = pdf_obj.metadata.get('Author', 'Unknown')

	# Extract first page text
	first_page_text = ""
	if pdf_obj.pages and len(pdf_obj.pages) > 0:
	first_page_text = pdf_obj.pages[0].extract_text() or ""

	pdf_data.append({
	'index': i,
	'title': title,
	'author': author,
	'num_pages': len(pdf_obj.pages) if hasattr(pdf_obj, 'pages') else 0,
	'first_page_text': first_page_text
	})

	# Progress indicator
	if i % 100 == 0:
	print(f"Processed {i} PDFs...")

	# Create DataFrame for analysis
	df = pd.DataFrame(pdf_data)
	print(f"Dataset summary:\n{df.describe()}")
	```

	### Filtering by Content Size
	```python
	# Filter papers by number of pages
	short_papers = []
	long_papers = []

	for sample in dataset['train']:
	pdf_obj = sample['pdf']
	if hasattr(pdf_obj, 'pages'):
	num_pages = len(pdf_obj.pages)
	if num_pages <= 10:
	short_papers.append(sample)
	elif num_pages >= 20:
	long_papers.append(sample)

	print(f"Short papers (≤10 pages): {len(short_papers)}")
	print(f"Long papers (≥20 pages): {len(long_papers)}")
	```

	## 📊 Dataset Statistics
	- Total PDFs: 7,814
	- Small Papers: 1,872 files (< 500KB)
	- Medium Papers: 4,605 files (500KB - 5MB)
	- Large Papers: 1,337 files (≥ 5MB)
	- Source: OpenReview platform
	- Domain: Machine Learning, AI, Computer Science

	## 🔬 Research Applications

	### Document Understanding
	```python
	# Extract paper structure
	for sample in dataset['train'][:5]:
	pdf_obj = sample['pdf']

	print(f"Pages: {len(pdf_obj.pages)}")

	# Analyze page structure
	for i, page in enumerate(pdf_obj.pages[:3]): # First 3 pages
	text = page.extract_text()
	if text:
	lines = text.split('\n')
	print(f"Page {i+1}: {len(lines)} lines")
	```

	### Academic Text Mining
	```python
	# Extract research topics and keywords
	import re

	keywords = {}
	for sample in dataset['train'][:100]: # Sample first 100 papers
	pdf_obj = sample['pdf']

	if pdf_obj.pages:
	# Extract abstract (usually on first page)
	first_page = pdf_obj.pages[0].extract_text()

	# Simple keyword extraction
	if 'abstract' in first_page.lower():
	# Extract common ML terms
	ml_terms = ['neural', 'learning', 'algorithm', 'model', 'training',
	'optimization', 'deep', 'network', 'classification', 'regression']

	for term in ml_terms:
	if term in first_page.lower():
	keywords[term] = keywords.get(term, 0) + 1

	print("Most common ML terms:")
	for term, count in sorted(keywords.items(), key=lambda x: x[1], reverse=True):
	print(f"{term}: {count}")
	```

	### Citation Analysis
	```python
	# Extract citation patterns
	import re

	citation_patterns = []

	for sample in dataset['train'][:50]:
	pdf_obj = sample['pdf']

	if pdf_obj.pages:
	# Look for references section
	for page in pdf_obj.pages:
	text = page.extract_text()
	if text and 'references' in text.lower():
	# Simple citation extraction
	citations = re.findall(r'\[\d+\]', text)
	citation_patterns.extend(citations)

	print(f"Found {len(citation_patterns)} citation references")
	```

	## 🛠️ Technical Details

	### PDF Processing
	- Library: Uses `pdfplumber` for PDF processing
	- Text Extraction: Full-text extraction with layout preservation
	- Metadata Access: Original document metadata when available
	- Image Support: Can extract images and figures (see pdfplumber docs)

	### Performance Tips
	```python
	# For large-scale processing, use streaming
	dataset_stream = load_dataset("sumuks/openreview-pdfs", streaming=True)

	# Process in batches
	batch_size = 10
	batch = []

	for sample in dataset_stream['train']:
	batch.append(sample)

	if len(batch) >= batch_size:
	# Process batch
	for item in batch:
	pdf_obj = item['pdf']
	# Your processing here

	batch = [] # Reset batch
	```

	### Memory Management
	```python
	# For memory-efficient processing
	def process_pdf_efficiently(sample):
	pdf_obj = sample['pdf']

	# Extract only what you need
	metadata = {
	'num_pages': len(pdf_obj.pages) if hasattr(pdf_obj, 'pages') else 0,
	'title': pdf_obj.metadata.get('Title', '') if hasattr(pdf_obj, 'metadata') and pdf_obj.metadata else ''
	}

	# Extract text page by page to avoid loading entire document
	first_page_text = ""
	if pdf_obj.pages:
	first_page_text = pdf_obj.pages[0].extract_text() or ""

	return metadata, first_page_text

	# Use generator for memory efficiency
	def pdf_generator():
	for sample in dataset['train']:
	yield process_pdf_efficiently(sample)
	```

	## 📈 Use Cases

	1. Large Language Model Training: Academic domain-specific text
	2. Information Retrieval: Document search and recommendation
	3. Research Analytics: Trend analysis and impact prediction
	4. Document Classification: Paper categorization by topic/methodology
	5. Citation Networks: Academic relationship mapping
	6. Text Summarization: Abstract and conclusion extraction
	7. Knowledge Extraction: Methodology and result mining

	## 🔍 Quality Notes

	- All PDFs are verified and accessible
	- Original filenames and metadata preserved where possible
	- Organized structure for efficient browsing and filtering
	- Compatible with standard PDF processing libraries

	## 📝 Citation

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

	```bibtex
	@misc{sanyal2025sparkscientificallycreativeidea,
	title={Spark: A System for Scientifically Creative Idea Generation},
	author={Aishik Sanyal and Samuel Schapiro and Sumuk Shashidhar and Royce Moon and Lav R. Varshney and Dilek Hakkani-Tur},
	year={2025},
	eprint={2504.20090},
	archivePrefix={arXiv},
	primaryClass={cs.AI},
	url={https://arxiv.org/abs/2504.20090},
	}
	```

	## 📄 License

	MIT License - Please respect the original licensing and copyright of individual papers. This dataset is provided for research and educational purposes.

	## 🙏 Acknowledgments

	- OpenReview: For hosting and providing access to academic research
	- Research Community: For contributing valuable academic content
	- HuggingFace: For providing the datasets infrastructure
	- PDF Processing Libraries: pdfplumber and related tools

	## 🐛 Issues & Support

	If you encounter any issues with the dataset:

	1. Check that you have the required dependencies: `pip install datasets pdfplumber`
	2. Ensure you're using the latest version of the datasets library
	3. For PDF-specific issues, refer to the pdfplumber documentation
	4. Report dataset issues on the HuggingFace discussion page

	## 🔄 Updates

	This dataset was created in 2025 and represents a snapshot of OpenReview content. For the most current research, please also check the live OpenReview platform.

	---

	Happy Researching! 🚀