Academic Paper Summarizer

Academic paper summarization with dynamic SOP selection based on paper topic classification. Supports method, dataset, multimodal, and other paper types with rigorous analysis templates.

Paper Summarize Skill

This skill provides academic-grade paper summarization with dynamic Standard Operating Procedure (SOP) selection based on paper topic classification.

Capabilities

• Dynamic SOP Selection: Automatically selects appropriate analysis template based on paper type (method, dataset, multimodal, etc.)
• Rigorous Analysis: Follows top-tier conference review criteria (NeurIPS/ICML/ICLR/ACL)
• Structured Output: Generates comprehensive summaries with methodology critique, experimental assessment, strengths/weaknesses
• Local File Storage: Saves summaries to organized directory structure with proper naming
• Prompt Tracking: Maintains record of actual prompts used for reproducibility
• Dataset Focus: Explicit attention to training/evaluation datasets used in experiments

Supported Paper Types

• method: Algorithm/architecture papers
• dataset: Dataset/benchmark papers
• multimodal: Cross-modal learning papers
• tech_report: System/model release papers
• application: Applied AI papers
• survey: Survey/review papers
• rl_alignment: RL/Alignment/Safety papers
• speech_audio: Speech/audio processing papers
• benchmark: Evaluation/benchmark papers
• analysis: Empirical analysis papers

Usage

Input Requirements

• Paper title, authors, abstract
• Topic classification (one of supported types)
• Research context (keywords, subtopics)

Output Format

• Local file: {paper_title}.md in research/{domain}/ai_summaries/
• Content structure:
  • Paper information (title, authors, venue, links)
  • Core contribution summary
  • Methodology critique (2000+ words)
  • Experimental assessment (1000+ words, with dataset focus)
  • Strengths and weaknesses
  • Critical questions for authors
  • Impact assessment

Quality Standards

• Methodology Critique: 2000+ characters, deep technical analysis including pipeline, novelty, mathematical principles, assumptions, prior art comparison, computational cost, and failure modes
• Experimental Assessment: 1000+ characters, rigorous evaluation with explicit focus on datasets used for training and testing, protocol rigor, baseline fairness, ablation completeness, and statistical significance
• Overall Analysis: 3000+ characters, critical perspective
• Technical Precision: Correct terminology, specific method names, exact metrics

Workflow Integration

This skill integrates with the broader research workflow:

1. Paper Discovery: Works with arXiv search results
2. Quality Filtering: Processes papers that pass relevance screening
3. Batch Processing: Can be called repeatedly for multiple papers
4. Report Generation: Outputs feed into final research report

Configuration

SOP templates are defined in:

• src/lib/agents/topic-sops.ts (primary location)
• summarization_prompt.ts (backup/reference)

Both files contain identical SOP definitions with shared output format requirements.

Examples

bash

# Summarize a method paper
paper_summarize --title "SongEcho: Cover Song Generation" --topic "method" --abstract "..." --authors "..."

# Summarize a dataset paper  
paper_summarize --title "MusicSem: Language-Audio Dataset" --topic "dataset" --abstract "..." --authors "..."

Files Created

• research/{domain}/ai_summaries/{paper_title}.md
• research/{domain}/prompts/{paper_title}_prompt.txt
• Directory structure automatically created if missing