paper-summarize-academic（论文总结学术）

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

# 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