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Paper2Web: Academic Homepage Generation

Overview

Paper2Web converts academic papers into interactive, explorable academic homepages. Unlike traditional approaches (direct generation, template-based, or HTML conversion), Paper2Web creates layout-aware, interactive websites through an iterative refinement process.

Core Capabilities

1. Layout-Aware Generation

  • Analyzes paper structure and content organization
  • Creates responsive, multi-section layouts
  • Adapts design based on paper type (research article, review, preprint, etc.)

2. Interactive Elements

  • Expandable sections for detailed content
  • Interactive figures and tables
  • Embedded citations and references
  • Navigation menu for easy browsing
  • Mobile-responsive design

3. Content Refinement

The system uses an iterative pipeline: 1. Initial content extraction and structuring 2. Layout generation with visual hierarchy 3. Interactive element integration 4. Aesthetic refinement 5. Quality assessment and validation

Usage

Basic Website Generation

python pipeline_all.py \
  --input-dir "path/to/papers" \
  --output-dir "path/to/output" \
  --model-choice 1

Parameters

  • --input-dir: Directory containing paper files (PDF or LaTeX)
  • --output-dir: Directory for generated website files
  • --model-choice: LLM model selection (1=GPT-4, 2=GPT-4.1)
  • --enable-logo-search: Use Google Search API to find institution logos (optional)

Input Format Requirements

Supported Input Formats: 1. LaTeX source (preferred for best results) - Main file: main.tex - Include all referenced figures, tables, and bibliography files - Organize in a single directory per paper

  1. PDF files
  2. High-quality PDF with selectable text
  3. Embedded figures should be high resolution
  4. Proper section headers and structure

Directory Structure:

input/
└── paper_name/
    ├── main.tex           # LaTeX source
    ├── bibliography.bib   # References
    ├── figures/           # Figure files
    │   ├── fig1.png
    │   └── fig2.pdf
    └── tables/            # Table files

Output Structure

Generated websites include:

output/paper_name/website/
├── index.html          # Main webpage
├── styles.css          # Styling
├── script.js           # Interactive features
├── assets/             # Images and media
│   ├── figures/
│   └── logos/
└── data/               # Structured data (optional)

Customization Options

Visual Design

The generated websites automatically include: - Professional color schemes based on paper content - Typography optimized for readability - Consistent spacing and visual hierarchy - Dark mode support (optional)

Content Sections

Standard sections include: - Abstract - Key findings/contributions - Methodology overview - Results and visualizations - Discussion and implications - References and citations - Author information and affiliations

Additional sections are automatically added based on paper content: - Code repositories - Dataset links - Supplementary materials - Related publications

Quality Assessment

Paper2Web includes built-in evaluation:

Aesthetic Metrics

  • Layout balance and spacing
  • Color harmony
  • Typography consistency
  • Visual hierarchy effectiveness

Informativeness Metrics

  • Content completeness
  • Key finding clarity
  • Method explanation adequacy
  • Results presentation quality

Technical Metrics

  • Page load time
  • Mobile responsiveness
  • Browser compatibility
  • Accessibility compliance

Advanced Features

Logo Discovery

When enabled with Google Search API: - Automatically finds institution logos - Matches author affiliations - Downloads and optimizes logo images - Integrates into website header

Citation Integration

  • Interactive reference list
  • Hover previews for citations
  • Links to DOI and external sources
  • Citation count tracking (if available)

Figure Enhancement

  • High-resolution figure rendering
  • Zoom and pan functionality
  • Caption and description integration
  • Multi-panel figure navigation

Best Practices

Input Preparation

  1. Use LaTeX when possible: Provides best structure extraction
  2. Include all assets: Figures, tables, and bibliography files
  3. Clean formatting: Remove compilation artifacts and temporary files
  4. High-quality figures: Use vector formats (PDF, SVG) when available

Model Selection

  • GPT-4: Best balance of quality and cost
  • GPT-4.1: Latest features, higher cost
  • GPT-3.5-turbo: Faster processing, acceptable for simple papers

Output Optimization

  1. Review generated content for accuracy
  2. Check that all figures render correctly
  3. Test interactive elements functionality
  4. Verify mobile responsiveness
  5. Validate external links

Limitations

  • Complex mathematical equations may require manual review
  • Multi-column layouts in PDF may affect extraction quality
  • Large papers (>50 pages) may require extended processing time
  • Some specialized figure types may need manual adjustment

Integration with Other Components

Paper2Web can be combined with: - Paper2Video: Generate companion video for the website - Paper2Poster: Create matching poster design - AutoPR: Generate promotional content linking to website

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