Scientific Summarization & Simplification

Purpose

Generate concise, accurate summaries of scientific papers, educational materials, and complex technical documents.

Key Datasets

• PubMed Summarization (ccdv/pubmed-summarization): Article-abstract pairs for biomedical summarization
• LearningQ (AngusGLChen/LearningQ): TED-Ed (7K) + Khan Academy (223K) educational QA for learning-oriented summarization

Protocol

1. Document analysis — Identify paper structure (IMRaD, review, case report)
2. Key claim extraction — Extract main findings, methods, and conclusions
3. Audience calibration — Adjust complexity to target audience (expert, student, public)
4. Summary generation — Structured summary with key takeaways
5. Fidelity check — Verify no hallucinated claims; all statements traceable to source