Summarization techniques

Summarization techniques

Overview

Summarization in CRISP-T provides concise representations of longer documents to accelerate qualitative review and triangulation. Current focus is on extractive summarization, with abstractive methods listed as prospective enhancements.

Extractive Summarization

• Activated via --summary in the analytical CLI.
• Produces key sentences reflective of document salience.
• Use alongside --assign to quickly scan topic-aligned documents.

Abstractive Summarization (Planned)

• Future integration may leverage transformer-based models for abstractive summarization (see Future Plan).
• Abstractive methods can propose paraphrased category labels but must be validated against original texts for grounded rigor.

Sense-making

• Summaries expedite memo writing, revealing recurring narrative motifs that can be checked against numeric structures (e.g., PCA clusters).
• Contrast summaries across clusters to identify differential framing of similar issues.

Examples

Business

Summaries of high-churn cluster documents frequently mention "complex onboarding"—triangulating with a decision tree split on onboarding_days.

Medical

Summaries of high-fatigue cases emphasize "poor sleep," "pain," and "low energy," aligning with quantitative predictors sleep_balance and inflammation_marker.

Best Practices

• Treat summaries as aids; always return to full text for coding.
• Store summaries alongside IDs to facilitate semantic chunk retrieval.

See Also

• NLP based techniques
• Latent Dirichlet Allocation
• Semantic filtering
• Human Sense-making and triangulation