PaperSorter

PaperSorter is an intelligent academic paper recommendation system that helps researchers stay up-to-date with relevant publications. It uses machine learning to filter RSS/Atom feeds and predict which papers match your research interests, then sends notifications to Slack or Discord for high-scoring articles.

Key Features

Multi-source feed aggregation: Fetches articles from RSS/Atom feeds (PubMed, bioRxiv, journal feeds, etc.)
ML-powered filtering: Uses XGBoost regression on article embeddings to predict interest levels
Flexible AI integration: Compatible with Solar LLM, Gemini, or any OpenAI-compatible generative AI API
Web-based labeling interface: Interactive UI for labeling articles and improving the model
Search from PDF (Paper Connect): Select text from PDFs to find semantically similar papers
Slack & Discord integration: Automated notifications for interesting papers with customizable thresholds
Scholarly database integration: Choose between Semantic Scholar (with TL;DR summaries) or OpenAlex (no API key needed) for metadata enrichment
Multi-channel support: Different models and thresholds for different research groups or topics
AI-powered content generation: Create concise summaries and visual infographics for article collections

📊 Architecture Overview: For a detailed view of how PaperSorter's components interact, see the Architecture Overview section below.

Installation

Option 1: Docker (Recommended)

The easiest way to get started with PaperSorter is using Docker:

# Clone the repository
git clone https://github.com/ChangLabSNU/PaperSorter.git
cd PaperSorter

# Configure environment
cp .env.example .env
# Edit .env with your API keys and settings

# Start all services
docker-compose up -d

# Initialize database
./papersorter-cli init

# Check service status
./papersorter-cli status

The application will be available at:

Local: http://localhost:5001
Production: https://your-domain.com (automatic HTTPS)

For detailed Docker setup, see DOCKER.md.

Option 2: Manual Installation

For development or custom deployments:

git clone https://github.com/ChangLabSNU/PaperSorter.git
cd PaperSorter
pip install -e .

System Requirements

Python 3.8+
PostgreSQL 12+ with pgvector extension
Modern web browser (for labeling interface)

Configuration

Docker Configuration

For Docker installations, configure via .env file:

cp .env.example .env
# Edit .env with your settings

Key settings in .env:

Database credentials
OAuth client IDs and secrets
API keys for embeddings and summarization
Domain and email for HTTPS

Manual Configuration

For manual installations, create config.yml (or specify with --config). See examples/config.yml for a complete example:

# Admin users - automatically promoted to admin on login (never demoted)
admin_users:
  - "admin@example.com"              # For Google/GitHub OAuth
  - "0000-0002-1825-0097@orcid.org"  # For ORCID OAuth

db:
  type: postgres
  host: localhost
  user: papersorter
  database: papersorter
  password: "your_password"

web:
  base_url: "https://your-domain.com"  # base URL for web interface
  flask_secret_key: "your_flask_secret_key"  # generate with secrets.token_hex(32)

# OAuth configuration - configure one or more providers
oauth:
  # Google OAuth
  google:
    client_id: "your_google_client_id.apps.googleusercontent.com"
    secret: "your_google_client_secret"

  # GitHub OAuth
  github:
    client_id: "your_github_oauth_client_id"
    secret: "your_github_oauth_client_secret"

  # ORCID OAuth (essential for academic users)
  orcid:
    client_id: "your_orcid_oauth_client_id"
    secret: "your_orcid_client_secret"
    sandbox: false  # Set to true for testing with sandbox.orcid.org

embedding_api:
  api_key: "your_api_key"
  api_url: "https://api.upstage.ai/v1"       # or your preferred provider
  model: "solar-embedding-1-large-passage"   # or your preferred model
  dimensions: 4096

summarization_api:
  api_key: "your_api_key"
  api_url: "https://generativelanguage.googleapis.com/v1beta/openai"  # For Gemini
  model: "gemini-2.5-pro"

# Scholarly database configuration (choose one provider)
scholarly_database:
  provider: "semantic_scholar"  # or "openalex"

  # Option 1: Semantic Scholar (provides TL;DR summaries, requires API key)
  semantic_scholar:
    api_key: "your_semantic_scholar_api_key"

  # Option 2: OpenAlex (no API key needed, just email)
  openalex:
    email: "your_email@example.com"  # Must be a valid email address

Database Setup

Docker Setup (Automatic)

Docker automatically sets up PostgreSQL with pgvector. Just run:

./papersorter-cli init

Manual Database Setup

1. Create PostgreSQL Database

# As PostgreSQL superuser:
sudo -u postgres psql <<EOF
CREATE USER papersorter WITH PASSWORD 'your_password';
CREATE DATABASE papersorter OWNER papersorter;
\c papersorter
CREATE EXTENSION vector;
GRANT ALL ON SCHEMA public TO papersorter;
EOF

Alternatively, if you have an existing database:

# Connect to your database and install pgvector
sudo -u postgres psql -d your_database -c "CREATE EXTENSION vector;"

2. Initialize Database Schema

papersorter init

To reinitialize (drops existing data):

papersorter init --drop-existing

Getting Started

Recommended Setup Workflow for New Users

For optimal model performance, follow this comprehensive workflow that uses a two-stage training process:

Stage 1: Initial Model Training (Similarity-based)

# 1. Initialize database (use ./papersorter-cli for Docker)
papersorter init  # or ./papersorter-cli init

# 2. Import PubMed data with specific ISSNs for your field (target ~10,000 articles)
# Find relevant journal ISSNs from the JOURNALS file or PubMed
# Docker:
./papersorter-cli import pubmed --issn 1476-4687 --issn 0036-8075 --issn 1097-6256 --files 20
# Manual:
papersorter import pubmed --issn 1476-4687 --issn 0036-8075 --issn 1097-6256 --files 20

# 3. Generate embeddings for all imported articles (essential for search and training)
# Docker:
./papersorter-cli predict --all  # or --count 10000
# Manual:
papersorter predict --all  # or --count 10000

# 4. Start web interface
# Docker: Already running at http://localhost:5001
# Manual:
papersorter serve --skip-authentication yourname@domain.com

# 5. Find and label 5-10 diverse "interested" papers using semantic search
# - Go to http://localhost:5001
# - Use search to find papers across different aspects of your research
# - Examples: "CRISPR gene editing", "protein folding", "neural networks"
# - Mark 5-10 papers as "Interested" (diversity is crucial!)

# 6. Create first labeling session based on similarity to interested papers
# Docker:
./papersorter-cli labeling create --sample-size 200
# Manual:
papersorter labeling create --sample-size 200

# 7. Complete the labeling session in the web interface
# - Go to http://localhost:5001/labeling
# - Label all 200 papers as "Interested" or "Not Interested"

# 8. Train your initial model
# Docker:
./papersorter-cli train --name "Initial Model v1"
# Manual:
papersorter train --name "Initial Model v1"

# 9. Generate predictions with the initial model
# Docker:
./papersorter-cli predict
# Manual:
papersorter predict

Stage 2: Model Refinement (Prediction-based) - Highly Recommended

# 10. Create second labeling session based on model predictions
# Docker:
./papersorter-cli labeling create --base-model 1 --sample-size 1000
# Manual:
papersorter labeling create --base-model 1 --sample-size 1000

# 11. Complete the second labeling session
# - Go to http://localhost:5001/labeling
# - Label all 1000 papers (this refines the model significantly)

# 12. Train improved model with larger dataset
# Docker:
./papersorter-cli train --name "Production Model v1"
# Manual:
papersorter train --name "Production Model v1"

# 13. Generate final predictions
# Docker:
./papersorter-cli predict
# Manual:
papersorter predict

# 14. Set up notifications and regular operations
# Configure channels, thresholds, and feeds in the web interface

Note: Stage 2 (steps 10-13) can be omitted but is highly recommended as it:

Prevents overfitting to the initial small set of papers
Creates a more generalized model
Significantly improves prediction accuracy

Detailed Setup Guide

1. Initialize and Import Data

# Initialize database
./papersorter-cli init  # Docker
papersorter init       # Manual

# Import from PubMed (recommended for initial data)
./papersorter-cli import pubmed  # Docker: Downloads 10 recent files, 10% sampling
papersorter import pubmed       # Manual

# OR fetch from configured RSS feeds
./papersorter-cli update  # Docker
papersorter update       # Manual

2. Generate Embeddings

For initial setup, generate embeddings for many articles:

# Generate embeddings for up to 10,000 articles
./papersorter-cli predict --count 10000  # Docker
papersorter predict --count 10000       # Manual

This step is crucial as it creates the vector representations needed for:

Semantic search in the web interface
Training the ML model
Finding similar papers

3. Label Training Data

Start the web interface:

# Docker: Already running at http://localhost:5001
# Manual:
papersorter serve
# Or for development without OAuth:
papersorter serve --skip-authentication yourname@domain.com

Navigate to http://localhost:5001 and:

Use semantic search to find papers in your research area
Mark papers as "Interested" (👍) for relevant research
Initially, you only need positive labels (the system handles the rest)

Tips for effective labeling:

Search for key terms in your field
Look for papers from authors you follow
Check papers from your favorite journals

4. Train the Model

All trained models must be registered with a name:

# Initial training (only positive labels needed)
papersorter train --name "Initial Model v1"

# The system will:
# - Detect if you have only positive labels
# - Automatically use unlabeled articles as negative examples
# - Apply appropriate weights to balance the training

5. Generate Predictions

After training, generate predictions for all articles:

papersorter predict

This will score all articles and queue high-scoring ones for notifications.

6. Iterative Improvement (Optional but Recommended)

For better accuracy, perform a second round:

# Review predictions in the web interface
# Mark false positives as "Not Interested"
# Mark false negatives as "Interested"

# Retrain with mixed labels
papersorter train --name "Improved Model v2"

# Generate new predictions
papersorter predict

Training Options

# Train on all users (default)
papersorter train --name "Consensus Model"

# Train on specific users
papersorter train --name "User1 Model" --user-id 1

# Train on multiple specific users
papersorter train --name "Team Model" --user-id 1 --user-id 2 --user-id 3

# Advanced options
papersorter train --name "Advanced Model" --rounds 500 --embeddings-table embeddings_v2

Model Management

The papersorter models command provides comprehensive model management:

# List all models with their status
papersorter models list
papersorter models list --active-only
papersorter models list --with-channels

# Show detailed model information
papersorter models show 1

# Activate/deactivate models
papersorter models activate 2
papersorter models deactivate 1

# Update model metadata
papersorter models modify 1 --name "Production Model v2" --notes "Improved accuracy"

# Export/import models for backup or sharing
papersorter models export 1 -o model_backup.pkl
papersorter models export 1 -o model_with_stats.pkl --include-predictions
papersorter models import model_backup.pkl --name "Restored Model"

# Validate model integrity
papersorter models validate  # Check all models
papersorter models validate 1  # Check specific model
papersorter models validate --fix-orphans  # Clean up orphaned files

# Delete models (with safety checks)
papersorter models delete 1  # Prompts for confirmation
papersorter models delete 1 --force  # Skip confirmation

Models are stored in the database with metadata and as pickle files in the configured model directory. Each model can be associated with different channels for multi-team support.

5. Deploy Web Interface for Production

For production use, deploy the web interface with a proper WSGI server and HTTPS:

Production Deployment

# Install uWSGI
pip install uwsgi

# Run with uWSGI
uwsgi --http :5001 --module PaperSorter.web.app:app --processes 4

# Configure reverse proxy (nginx example) with SSL:
# server {
#     listen 443 ssl;
#     server_name your-domain.com;
#     ssl_certificate /path/to/cert.pem;
#     ssl_certificate_key /path/to/key.pem;
#
#     location / {
#         proxy_pass http://localhost:5001;
#         proxy_set_header Host $host;
#         proxy_set_header X-Real-IP $remote_addr;
#     }
# }

Development/Testing

For local development or testing with external services:

# Option 1: Local development
papersorter serve --port 5001 --debug

# Option 2: Testing with HTTPS (using ngrok)
papersorter serve --port 5001
ngrok http 5001  # Creates HTTPS tunnel to your local server

6. Configure Notifications (Slack or Discord)

In the web interface:

Go to Settings → Channels
Add a webhook URL (Slack or Discord)
Set the score threshold (e.g., 0.7)
Select which model to use

The system automatically detects the webhook type based on the URL:

Slack webhooks: URLs ending with slack.com
Discord webhooks: URLs ending with discord.com or discordapp.com

For Slack

Get a webhook URL from your Slack workspace:

Go to your Slack App settings or create a new app
Enable Incoming Webhooks
Create a webhook for your desired channel
Copy the webhook URL (format: https://hooks.slack.com/services/...)

Optional: Enable Slack Interactivity

To add interactive buttons to Slack messages:

Create a Slack App with Interactive Components enabled
Configure the Request URL in your Slack App:
- Set to: https://your-domain.com/slack-interactivity (must be HTTPS)

For Discord

Get a webhook URL from your Discord server:

Go to your Discord channel settings
Navigate to Integrations → Webhooks
Create a new webhook or use an existing one
Copy the webhook URL (format: https://discord.com/api/webhooks/...)

Discord notifications include:

Rich embeds with color-coded scores (green/yellow/red)
Visual score indicators (🟢🟡🔴)
Markdown-formatted action links
Timestamp and model information

7. Regular Operation

Docker (Automatic Scheduling)

Docker includes a scheduler container that automatically runs:

Update: Every 3 hours (fetch new articles)
Broadcast: Every hour (send notifications)

No additional setup required - these run automatically when Docker is running.

Manual Setup (Cron)

For manual installations, set up cron jobs:

# Fetch new articles and generate predictions (every 3 hours)
papersorter update

# Send notifications for high-scoring articles (run hourly - channels have individual hour restrictions)
papersorter broadcast

Example cron configuration (see examples/ directory for complete scripts with log rotation):

30 */3 * * * /path/to/papersorter/examples/cron-update.sh
0 * * * * /path/to/papersorter/examples/cron-broadcast.sh  # Run every hour

Note: Broadcast hours are now configured per channel in the web interface. The broadcast task can run every hour and will automatically skip channels outside their configured broadcast hours.

Scholarly Database Providers

PaperSorter can enrich article metadata using either Semantic Scholar or OpenAlex:

Semantic Scholar

Pros: Provides TL;DR summaries, comprehensive metadata
Cons: Requires API key (free with registration)
Best for: Users who want article summaries

Configuration:

scholarly_database:
  provider: "semantic_scholar"
  semantic_scholar:
    api_key: "your_api_key"

OpenAlex

Pros: No API key required, just email address; larger database
Cons: No TL;DR summaries
Best for: Quick setup, broader coverage

Configuration:

scholarly_database:
  provider: "openalex"
  openalex:
    email: "your_email@example.com"  # Must be valid

Both providers will enrich your articles with:

Corrected author names
Journal/venue information
Full abstracts
Publication dates

Command Reference

Core Commands

papersorter init - Initialize database schema
papersorter update - Fetch new articles and generate embeddings
papersorter train - Train or retrain the prediction model
papersorter predict - Generate predictions for articles
papersorter broadcast - Send notifications (Slack/Discord) for interesting articles
papersorter serve - Start the web interface for labeling and configuration
papersorter models - Manage trained models (list, show, activate, delete, export, import)

Common Options

All commands support:

--config PATH - Configuration file path (default: config.yml)
--log-file PATH - Log output to file
-q, --quiet - Suppress console output

Command-Specific Options

update:

--batch-size N - Processing batch size
--limit-sources N - Maximum number of feeds to process
--check-interval-hours N - Hours between checks for the same feed

train:

--name NAME - Model name for database registration (required)
-r, --rounds N - XGBoost training rounds (default: 1000)
--user-id ID - Train on specific user(s), can be repeated
--embeddings-table NAME - Embeddings table name (default: embeddings)

predict:

--count N - Number of articles to process
--all - Process all articles without limit
--force - Force re-prediction even if predictions exist

models:

list - List all models with status and statistics
show ID - Show detailed model information
modify ID --name/--notes - Update model metadata
activate ID - Activate a model
deactivate ID - Deactivate a model
delete ID - Delete a model (with safety checks)
export ID -o FILE - Export model with metadata
import FILE - Import model from file
validate [ID] - Validate model file integrity

broadcast:

--limit N - Maximum items to process per channel
--max-content-length N - Maximum content length for messages
--clear-old-days N - Clear broadcasts older than N days (default: 30)

serve:

--host ADDRESS - Bind address (default: 0.0.0.0)
--port N - Port number (default: 5001)
--debug - Enable Flask debug mode
--skip-authentication USERNAME - Bypass OAuth and auto-login as admin user (development only)

Web Interface Features

The web interface (http://localhost:5001) provides:

Main Paper View

Browse all articles with predictions
Interactive labeling (Interested/Not Interested)
Semantic article search
Shareable search URLs
Filter by date, score, or label status

Article Features

View full abstracts and metadata
Find similar articles
Direct links to paper PDFs
Semantic Scholar integration for citations

Search from PDF (Paper Connect)

Upload and view PDF files directly in browser
Select text from PDFs to automatically search for similar papers
Split-pane interface with resizable columns
No server-side storage - PDFs processed entirely in browser
Automatic search triggers when selecting 10+ characters

AI-Powered Tools

Generate article summaries
Create visual infographics for article collections

Admin Settings

Manage feeds
Configure notification channels
View model performance
User management
System event logs

Improving Model Performance

Regular labeling: Continue labeling new articles through the web interface
Balanced labels: Maintain a good ratio of positive/negative examples
Retrain periodically: Run papersorter train after adding new labels
Monitor performance: Check ROC-AUC scores and adjust thresholds accordingly

Architecture Overview

PaperSorter consists of several key components that work together to fetch, analyze, and distribute academic papers:

┌────────────────────────────────────────────────────────────────────────────────┐
│                            EXTERNAL SOURCES / SERVICES                         │
├────────────────────────────────────────────────────────────────────────────────┤
│  ┌──────────────┐  ┌──────────────┐  ┌──────────────┐  ┌────────────────────┐  │
│  │ RSS/Atom     │  │ Embedding    │  │ Chat API     │  │ Scholarly APIs     │  │
│  │ Feeds        │  │ API          │  │ (OpenAI-     │  │ • Semantic Scholar │  │
│  │ • arXiv      │  │ (OpenAI-     │  │  compatible) │  │ • OpenAlex         │  │
│  │ • bioRxiv    │  │  compatible) │  │              │  │                    │  │
│  │ • PubMed     │  │              │  │ AI Summary   │  │ Paper Metadata     │  │
│  │ • Journals   │  │ Text Analysis│  │ Generation   │  │ Enrichment         │  │
│  └──────┬───────┘  └──────▲───────┘  └──────▲───────┘  └─────────▲──────────┘  │
└─────────┼─────────────────┼─────────────────┼────────────────────┼─────────────┘
          ▼                 │                 │                    │
  ┌────────────────────────────────────────────────────────────────────────┐
  │                        PAPERSORTER CORE SYSTEM                         │
  ├────────────────────────────────────────────────────────────────────────┤
  │  ┌─────────────────────── Background Workflows ─────────────────────┐  │
  │  │  ┌─────────────────┐   ┌──────────────────┐  ┌────────────────┐  │  │
  │  │  │ Update Task     │   │ Train Task       │  │ Broadcast Task │  │  │
  │  │  │ • RSS Retrieval │   │ • XGBoost        │  │ • Notification │  │  │
  │  │  │ • Embedding     │──▶│   Classification │  │   Dispatch     │  │  │
  │  │  │   Generation    │   │ • Model          │  │ • Queue        │  │  │
  │  │  │ • Queue Mgmt    │   │   Training       │  │   Processing   │  │  │
  │  │  └────────┬────────┘   └────────▲─────────┘  └────────┬───────┘  │  │
  │  └───────────┼─────────────────────┼─────────────────────┼──────────┘  │
  │              ▼                     │                     ▼             │
  │      ┌─────────────────────────────────────────────────────────────┐   │
  │      │          PostgreSQL Database + pgvector extension           │   │
  │      │  ┌───────────────────────────────────────────────────────┐  │   │
  │      │  │ • Articles      • Embeddings     • User Preferences   │  │   │
  │      │  │ • Predictions   • Channels       • Broadcast Queue    │  │   │
  │      │  └───────────────────────────────────────────────────────┘  │   │
  │      └──────────────────────────▲──────────────────────────────────┘   │
  │      ┌──────────────────────────┴─────────────────────────────────┐    │
  │      │              Web Interface (Flask Application)             │    │
  │      │  ┌─────────────────────────────────────────────────────┐   │    │
  │      │  │ • Paper List Display       • Paper Labeling         │   │    │
  │      │  │ • Search Interface         • Admin Settings         │   │    │
  │      │  └─────────────────────────────────────────────────────┘   │    │
  │      └─────────────────────▲─────────┬────────────────────────────┘    │
  └────────────────────────────┼─────────┼─────────────────────────────────┘
   ┌───── OAuth Providers ─────▼──┐  ┌───▼─── Notification Services ─────┐
   │  ┌──────────┐  ┌──────────┐  │  │  ┌──────────┐  ┌──────────┐       │
   │  │  Google  │  │  GitHub  │  │  │  │  Slack   │  │ Discord  │       │
   │  │  OAuth   │  │  OAuth   │  │  │  │(Optional)│  │(Optional)│       │
   │  └──────────┘  └──────────┘  │  │  └──────────┘  └──────────┘       │
   │         ┌──────────┐         │  │       ┌────────────────────────┐  │
   │         │  ORCID   │         │  │       │ SMTP Server (Optional) │  │
   │         │  OAuth   │         │  │       │  Email Notifications   │  │
   │         └──────────┘         │  │       └────────────────────────┘  │
   └──────────────────────────────┘  └───────────────────────────────────┘

Key Components:

RSS Feed System: Modular architecture for different feed sources (RSS/Atom)
Background Workflows:
- Update Task: Fetches articles, generates embeddings, manages broadcast queue
- Train Task: Trains XGBoost model using labeled data
- Broadcast Task: Processes queue and sends notifications
Embedding Pipeline: Generates vector representations using OpenAI-compatible APIs
ML Predictor: XGBoost model trained on user preferences for interest prediction
PostgreSQL + pgvector: Efficient storage and similarity search for embeddings
Flask Web Application: Modern interface with OAuth authentication (ORCID, Google, GitHub)
Notification System: Multi-channel support for Slack, Discord, and email notifications

License

MIT License - see LICENSE file for details

Author

Hyeshik Chang hyeshik@snu.ac.kr

Contributing

Contributions are welcome! Please feel free to submit issues or pull requests on GitHub.

Name		Name	Last commit message	Last commit date
Latest commit History 282 Commits
.github/workflows		.github/workflows
PaperSorter		PaperSorter
docker		docker
docs		docs
examples		examples
migrations		migrations
tools		tools
.dockerignore		.dockerignore
.env.example		.env.example
.gitignore		.gitignore
CHANGELOG.md		CHANGELOG.md
CLAUDE.md		CLAUDE.md
DOCKER.md		DOCKER.md
Dockerfile		Dockerfile
Dockerfile.scheduler		Dockerfile.scheduler
LICENSE.txt		LICENSE.txt
MANIFEST.in		MANIFEST.in
README.md		README.md
SQL_SCHEMA.sql		SQL_SCHEMA.sql
docker-compose.prod.yml		docker-compose.prod.yml
docker-compose.yml		docker-compose.yml
papersorter-cli		papersorter-cli
pyproject.toml		pyproject.toml
requirements.txt		requirements.txt
setup.py		setup.py

License

ChangLabSNU/PaperSorter

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PaperSorter

Key Features

Installation

Option 1: Docker (Recommended)

Option 2: Manual Installation

System Requirements

Configuration

Docker Configuration

Manual Configuration

Database Setup

Docker Setup (Automatic)

Manual Database Setup

1. Create PostgreSQL Database

2. Initialize Database Schema

Getting Started

Recommended Setup Workflow for New Users

Stage 1: Initial Model Training (Similarity-based)

Stage 2: Model Refinement (Prediction-based) - Highly Recommended

Detailed Setup Guide

1. Initialize and Import Data

2. Generate Embeddings

3. Label Training Data

4. Train the Model

5. Generate Predictions

6. Iterative Improvement (Optional but Recommended)

Training Options

Model Management

5. Deploy Web Interface for Production

Production Deployment

Development/Testing

6. Configure Notifications (Slack or Discord)

For Slack

For Discord

7. Regular Operation

Docker (Automatic Scheduling)

Manual Setup (Cron)

Scholarly Database Providers

Semantic Scholar

OpenAlex

Command Reference

Core Commands

Common Options

Command-Specific Options

Web Interface Features

Main Paper View

Article Features

Search from PDF (Paper Connect)

AI-Powered Tools

Admin Settings

Improving Model Performance

Architecture Overview

Key Components:

License

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Contributing

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