algo-nlp-summarization
Text Summarization
Overview
Text summarization condenses documents while preserving key information. Extractive: selects and concatenates important sentences from the original. Abstractive: generates new text that paraphrases the content. Extractive is simpler and more faithful; abstractive is more fluent but may hallucinate.
When to Use
Trigger conditions:
- Condensing long documents, reports, or article collections
- Building automated summary pipelines for content curation
- Comparing extractive vs abstractive approaches for a use case
When NOT to use:
- When full document understanding is needed (summarization loses detail)
- For structured data extraction (use NER or information extraction)
Algorithm
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