PaperScorer

## Overview PaperScorer is a browser extension that helps researchers quickly identify relevant papers by using AI to evaluate and score academic papers based on your research topic. It analyzes each paper's title and abstract, then displays a color-coded relevance score directly in search results, making literature review more efficient. For more details, including the source code and documentation, please visit the GitHub repository at https://github.com/jxrjxrjxr/paperscorer ## Features - **AI-Powered Relevance Scoring**: Uses large language models to analyze paper titles and abstracts against your research topic - **Multi-dimensional Evaluation**: Scores papers across five customizable dimensions: - Topic Similarity (30%) - Methodological Relevance (25%) - Research Novelty (20%) - Research Field Alignment (15%) - Problem Significance (10%) - **Visual Scoring System**: Color-coded relevance indicators make it easy to quickly identify the most relevant papers: - 🟢 Green (90-100): Highly relevant - 🟡 Yellow (70-89): Moderately relevant - 🟠 Orange (50-69): Somewhat relevant - 🔴 Red (< 50): Less relevant - **Interactive Details**: Hover over scores to see a summary, click to view detailed scoring across all dimensions - **Search Expression Generator**: Helps create optimized search queries for your research topic - **Customizable Evaluation Criteria**: Define your own scoring dimensions and adjust weights based on your priorities