"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

IRON LAW: Abstractive Summarization Can HALLUCINATE
Abstractive models may generate fluent text containing facts NOT in
the source. Always verify key claims in abstractive summaries against
the original document. For high-stakes use cases (legal, medical),
prefer extractive or use abstractive with factual consistency checking.

Phase 1: Input Validation

Determine: input length, target summary length (ratio or word count), single-doc vs multi-doc, domain. Gate: Input text available, target length defined.

Phase 2: Core Algorithm

Extractive (TextRank/LexRank):

1. Split document into sentences
2. Build similarity graph (sentence nodes, cosine similarity edges)
3. Run PageRank on sentence graph
4. Select top-k sentences by rank, reorder by original position

Abstractive (transformer-based):

1. Use pre-trained model (BART, T5, Pegasus)
2. Encode input document (handle length limits with chunking if needed)
3. Generate summary with beam search
4. Post-process: check for repetition, factual consistency

Phase 3: Verification

Evaluate: ROUGE scores (ROUGE-1, ROUGE-2, ROUGE-L) against reference summaries. Manual check for factual accuracy and coherence. Gate: ROUGE scores reasonable for domain, no hallucinations in spot-check.