Initial commit
This commit is contained in:
Zhongwei Li
436
skills/paper-2-web/references/usage_examples.md
Normal file
436
skills/paper-2-web/references/usage_examples.md
Normal file
@@ -0,0 +1,436 @@
|
||||
# Usage Examples and Workflows
|
||||
|
||||
## Complete Workflow Examples
|
||||
|
||||
### Example 1: Conference Presentation Package
|
||||
|
||||
**Scenario**: Preparing for a major conference presentation with website, poster, and video.
|
||||
|
||||
**User Request**: "I need to create a complete presentation package for my NeurIPS paper submission. Generate a website, poster, and video presentation."
|
||||
|
||||
**Workflow**:
|
||||
|
||||
```bash
|
||||
# Step 1: Organize paper files
|
||||
mkdir -p input/neurips2025_paper
|
||||
cp main.tex input/neurips2025_paper/
|
||||
cp -r figures/ input/neurips2025_paper/
|
||||
cp -r tables/ input/neurips2025_paper/
|
||||
cp bibliography.bib input/neurips2025_paper/
|
||||
|
||||
# Step 2: Generate all components
|
||||
python pipeline_all.py \
|
||||
--input-dir input/neurips2025_paper \
|
||||
--output-dir output/ \
|
||||
--model-choice 1 \
|
||||
--generate-website \
|
||||
--generate-poster \
|
||||
--generate-video \
|
||||
--poster-width-inches 48 \
|
||||
--poster-height-inches 36 \
|
||||
--enable-logo-search
|
||||
|
||||
# Step 3: Review outputs
|
||||
ls -R output/neurips2025_paper/
|
||||
# - website/index.html
|
||||
# - poster/poster_final.pdf
|
||||
# - video/final_video.mp4
|
||||
```
|
||||
|
||||
**Output**:
|
||||
- Interactive website showcasing research
|
||||
- 4'×3' conference poster (print-ready)
|
||||
- 12-minute presentation video
|
||||
- Processing time: ~45 minutes (without talking-head)
|
||||
|
||||
---
|
||||
|
||||
### Example 2: Quick Website for Preprint
|
||||
|
||||
**Scenario**: Creating an explorable homepage for a bioRxiv preprint.
|
||||
|
||||
**User Request**: "Convert my genomics preprint to an interactive website to accompany the bioRxiv submission."
|
||||
|
||||
**Workflow**:
|
||||
|
||||
```bash
|
||||
# Using PDF input (LaTeX not available)
|
||||
python pipeline_all.py \
|
||||
--input-dir papers/genomics_preprint/ \
|
||||
--output-dir output/genomics_web/ \
|
||||
--model-choice 1 \
|
||||
--generate-website
|
||||
|
||||
# Deploy to GitHub Pages or personal server
|
||||
cd output/genomics_web/website/
|
||||
# Add link to bioRxiv paper, data repositories, code
|
||||
# Upload to hosting service
|
||||
```
|
||||
|
||||
**Tips**:
|
||||
- Include links to bioRxiv DOI
|
||||
- Add GitHub repository links
|
||||
- Include data availability section
|
||||
- Embed interactive visualizations if possible
|
||||
|
||||
---
|
||||
|
||||
### Example 3: Video Abstract for Journal Submission
|
||||
|
||||
**Scenario**: Creating a video abstract for a journal that encourages multimedia submissions.
|
||||
|
||||
**User Request**: "Generate a 5-minute video abstract for my Nature Communications submission."
|
||||
|
||||
**Workflow**:
|
||||
|
||||
```bash
|
||||
# Generate concise video focusing on key findings
|
||||
python pipeline_light.py \
|
||||
--model_name_t gpt-4.1 \
|
||||
--model_name_v gpt-4.1 \
|
||||
--result_dir output/video_abstract/ \
|
||||
--paper_latex_root papers/nature_comms/ \
|
||||
--video-duration 300 \
|
||||
--slides-per-minute 3
|
||||
|
||||
# Optional: Add custom intro/outro slides
|
||||
# Optional: Include talking-head for introduction
|
||||
```
|
||||
|
||||
**Output**:
|
||||
- 5-minute video abstract
|
||||
- Focus on visual results
|
||||
- Clear, accessible narration
|
||||
- Journal-ready format
|
||||
|
||||
---
|
||||
|
||||
### Example 4: Multi-Paper Website Generation
|
||||
|
||||
**Scenario**: Creating websites for multiple papers from a research group.
|
||||
|
||||
**User Request**: "Generate websites for all 5 papers our lab published this year."
|
||||
|
||||
**Workflow**:
|
||||
|
||||
```bash
|
||||
# Organize papers
|
||||
mkdir -p batch_input/
|
||||
# Create subdirectories: paper1/, paper2/, paper3/, paper4/, paper5/
|
||||
# Each with their LaTeX sources
|
||||
|
||||
# Batch process
|
||||
python pipeline_all.py \
|
||||
--input-dir batch_input/ \
|
||||
--output-dir batch_output/ \
|
||||
--model-choice 1 \
|
||||
--generate-website \
|
||||
--enable-logo-search
|
||||
|
||||
# Creates:
|
||||
# batch_output/paper1/website/
|
||||
# batch_output/paper2/website/
|
||||
# batch_output/paper3/website/
|
||||
# batch_output/paper4/website/
|
||||
# batch_output/paper5/website/
|
||||
```
|
||||
|
||||
**Best Practice**:
|
||||
- Use consistent naming conventions
|
||||
- Process overnight for large batches
|
||||
- Review each website for accuracy
|
||||
- Deploy to unified lab website
|
||||
|
||||
---
|
||||
|
||||
### Example 5: Poster for Virtual Conference
|
||||
|
||||
**Scenario**: Creating a digital poster for a virtual conference with interactive elements.
|
||||
|
||||
**User Request**: "Create a poster for the virtual ISMB conference with clickable links to code and data."
|
||||
|
||||
**Workflow**:
|
||||
|
||||
```bash
|
||||
# Generate poster with QR codes and links
|
||||
python pipeline_all.py \
|
||||
--input-dir papers/ismb_submission/ \
|
||||
--output-dir output/ismb_poster/ \
|
||||
--model-choice 1 \
|
||||
--generate-poster \
|
||||
--poster-width-inches 48 \
|
||||
--poster-height-inches 36 \
|
||||
--enable-qr-codes
|
||||
|
||||
# Manually add QR codes to:
|
||||
# - GitHub repository
|
||||
# - Interactive results dashboard
|
||||
# - Supplementary data
|
||||
# - Video presentation
|
||||
```
|
||||
|
||||
**Digital Enhancements**:
|
||||
- PDF with embedded hyperlinks
|
||||
- High-resolution PNG for virtual platform
|
||||
- Separate PDF with video links for download
|
||||
|
||||
---
|
||||
|
||||
### Example 6: Promotional Video Clip
|
||||
|
||||
**Scenario**: Creating a short promotional video for social media.
|
||||
|
||||
**User Request**: "Generate a 2-minute highlight video of our Cell paper for Twitter."
|
||||
|
||||
**Workflow**:
|
||||
|
||||
```bash
|
||||
# Generate short, engaging video
|
||||
python pipeline_light.py \
|
||||
--model_name_t gpt-4.1 \
|
||||
--model_name_v gpt-4.1 \
|
||||
--result_dir output/promo_video/ \
|
||||
--paper_latex_root papers/cell_paper/ \
|
||||
--video-duration 120 \
|
||||
--presentation-style public
|
||||
|
||||
# Post-process:
|
||||
# - Extract key 30-second clip for Twitter
|
||||
# - Add captions for sound-off viewing
|
||||
# - Optimize file size for social media
|
||||
```
|
||||
|
||||
**Social Media Optimization**:
|
||||
- Square format (1:1) for Instagram
|
||||
- Horizontal format (16:9) for Twitter/LinkedIn
|
||||
- Vertical format (9:16) for TikTok/Stories
|
||||
- Add text overlays for key findings
|
||||
|
||||
---
|
||||
|
||||
## Common Use Case Patterns
|
||||
|
||||
### Pattern 1: LaTeX Paper → Full Package
|
||||
|
||||
**Input**: LaTeX source with all assets
|
||||
**Output**: Website + Poster + Video
|
||||
**Time**: 45-90 minutes
|
||||
**Best for**: Major publications, conference presentations
|
||||
|
||||
```bash
|
||||
python pipeline_all.py \
|
||||
--input-dir [latex_dir] \
|
||||
--output-dir [output_dir] \
|
||||
--model-choice 1 \
|
||||
--generate-website \
|
||||
--generate-poster \
|
||||
--generate-video
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
### Pattern 2: PDF → Interactive Website
|
||||
|
||||
**Input**: Published PDF paper
|
||||
**Output**: Explorable website
|
||||
**Time**: 15-30 minutes
|
||||
**Best for**: Post-publication promotion, preprint enhancement
|
||||
|
||||
```bash
|
||||
python pipeline_all.py \
|
||||
--input-dir [pdf_dir] \
|
||||
--output-dir [output_dir] \
|
||||
--model-choice 1 \
|
||||
--generate-website
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
### Pattern 3: LaTeX → Conference Poster
|
||||
|
||||
**Input**: LaTeX paper
|
||||
**Output**: Print-ready poster (custom size)
|
||||
**Time**: 10-20 minutes
|
||||
**Best for**: Conference poster sessions
|
||||
|
||||
```bash
|
||||
python pipeline_all.py \
|
||||
--input-dir [latex_dir] \
|
||||
--output-dir [output_dir] \
|
||||
--model-choice 1 \
|
||||
--generate-poster \
|
||||
--poster-width-inches [width] \
|
||||
--poster-height-inches [height]
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
### Pattern 4: LaTeX → Presentation Video
|
||||
|
||||
**Input**: LaTeX paper
|
||||
**Output**: Narrated presentation video
|
||||
**Time**: 20-60 minutes (without talking-head)
|
||||
**Best for**: Video abstracts, online presentations, course materials
|
||||
|
||||
```bash
|
||||
python pipeline_light.py \
|
||||
--model_name_t gpt-4.1 \
|
||||
--model_name_v gpt-4.1 \
|
||||
--result_dir [output_dir] \
|
||||
--paper_latex_root [latex_dir]
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Platform-Specific Outputs
|
||||
|
||||
### Twitter/X Promotional Content
|
||||
|
||||
The system auto-detects Twitter targeting for numeric folder names:
|
||||
|
||||
```bash
|
||||
# Create Twitter-optimized content
|
||||
mkdir -p input/001_twitter_post/
|
||||
# System generates English promotional content
|
||||
```
|
||||
|
||||
**Generated Output**:
|
||||
- Short, engaging summary
|
||||
- Key figure highlights
|
||||
- Hashtag recommendations
|
||||
- Thread-ready format
|
||||
|
||||
---
|
||||
|
||||
### Xiaohongshu (小红书) Content
|
||||
|
||||
For Chinese social media, use alphanumeric folder names:
|
||||
|
||||
```bash
|
||||
# Create Xiaohongshu-optimized content
|
||||
mkdir -p input/xhs_genomics/
|
||||
# System generates Chinese promotional content
|
||||
```
|
||||
|
||||
**Generated Output**:
|
||||
- Chinese language content
|
||||
- Platform-appropriate formatting
|
||||
- Visual-first presentation
|
||||
- Engagement optimizations
|
||||
|
||||
---
|
||||
|
||||
## Troubleshooting Common Scenarios
|
||||
|
||||
### Scenario: Large Paper (>50 pages)
|
||||
|
||||
**Challenge**: Processing time and content selection
|
||||
**Solution**:
|
||||
```bash
|
||||
# Option 1: Focus on key sections
|
||||
# Edit LaTeX to comment out less critical sections
|
||||
|
||||
# Option 2: Process in parts
|
||||
# Generate website for overview
|
||||
# Generate separate detailed videos for methods/results
|
||||
|
||||
# Option 3: Use faster model for initial pass
|
||||
# Review and regenerate critical components with better model
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
### Scenario: Complex Mathematical Content
|
||||
|
||||
**Challenge**: Equations may not render perfectly
|
||||
**Solution**:
|
||||
- Use LaTeX input (not PDF) for best equation handling
|
||||
- Review generated content for equation accuracy
|
||||
- Manually adjust complex equations if needed
|
||||
- Consider using figure screenshots for critical equations
|
||||
|
||||
---
|
||||
|
||||
### Scenario: Non-Standard Paper Structure
|
||||
|
||||
**Challenge**: Paper doesn't follow standard IMRAD format
|
||||
**Solution**:
|
||||
- Provide custom section guidance in paper metadata
|
||||
- Review generated structure and adjust
|
||||
- Use more powerful model (GPT-4.1) for better adaptation
|
||||
- Consider manual section annotation in LaTeX comments
|
||||
|
||||
---
|
||||
|
||||
### Scenario: Limited API Budget
|
||||
|
||||
**Challenge**: Reducing costs while maintaining quality
|
||||
**Solution**:
|
||||
```bash
|
||||
# Use GPT-3.5-turbo for simple papers
|
||||
python pipeline_all.py \
|
||||
--input-dir [paper_dir] \
|
||||
--output-dir [output_dir] \
|
||||
--model-choice 3
|
||||
|
||||
# Generate only needed components
|
||||
# Website-only (cheapest)
|
||||
# Poster-only (moderate)
|
||||
# Video without talking-head (moderate)
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
### Scenario: Tight Deadline
|
||||
|
||||
**Challenge**: Need outputs quickly
|
||||
**Solution**:
|
||||
```bash
|
||||
# Parallel processing if multiple papers
|
||||
# Use faster models (GPT-3.5-turbo)
|
||||
# Generate only essential component first
|
||||
# Skip optional features (logo search, talking-head)
|
||||
|
||||
python pipeline_light.py \
|
||||
--model_name_t gpt-3.5-turbo \
|
||||
--model_name_v gpt-3.5-turbo \
|
||||
--result_dir [output_dir] \
|
||||
--paper_latex_root [latex_dir]
|
||||
```
|
||||
|
||||
**Priority Order**:
|
||||
1. Website (fastest, most versatile)
|
||||
2. Poster (moderate speed, print deadline)
|
||||
3. Video (slowest, can be generated later)
|
||||
|
||||
---
|
||||
|
||||
## Quality Optimization Tips
|
||||
|
||||
### For Best Website Results
|
||||
1. Use LaTeX input with all assets
|
||||
2. Include high-resolution figures
|
||||
3. Ensure paper has clear section structure
|
||||
4. Enable logo search for professional appearance
|
||||
5. Review and test all interactive elements
|
||||
|
||||
### For Best Poster Results
|
||||
1. Provide high-resolution figures (300+ DPI)
|
||||
2. Specify exact poster dimensions needed
|
||||
3. Include institution branding information
|
||||
4. Use professional color scheme
|
||||
5. Test print small preview before full poster
|
||||
|
||||
### For Best Video Results
|
||||
1. Use LaTeX for clearest content extraction
|
||||
2. Specify target duration appropriately
|
||||
3. Review script before video generation
|
||||
4. Choose appropriate presentation style
|
||||
5. Test audio quality and pacing
|
||||
|
||||
### For Best Overall Results
|
||||
1. Start with clean, well-organized LaTeX source
|
||||
2. Use GPT-4 or GPT-4.1 for highest quality
|
||||
3. Review all outputs before finalizing
|
||||
4. Iterate on any component that needs adjustment
|
||||
5. Combine components for cohesive presentation package
|
||||
Reference in New Issue
Block a user