Extractive Summarization

Extractive summarization is a natural language processing technique that produces a summary by identifying and extracting the most important sentences from a source document, presenting them in order without modification.

How it differs from abstractive summarization

Extractive summarization copies existing sentences verbatim. Abstractive summarization writes new sentences that convey the same meaning. Extractive is like highlighting passages in a textbook. Abstractive is like writing your own study notes in your own words.

How extractive summarization works

The process involves two main steps. First, the system scores each sentence in the document for importance. Second, it selects the top-scoring sentences and arranges them to form a coherent summary.

Sentence scoring can use various signals. Statistical methods measure word frequency and position — sentences containing frequently used terms and sentences near the beginning of sections tend to be more important. Machine learning methods train models to predict which sentences humans would include in a summary. Graph-based methods like TextRank build a network of sentence similarities and identify the most central sentences.

Advantages of extractive summarization

• No hallucination risk: Since every sentence comes directly from the source, the summary cannot contain fabricated information.
• Faithful to source: The original author's phrasing and nuance are preserved exactly.
• Computationally simpler: Extractive methods are generally faster and cheaper to run than generative approaches.
• Verifiable: Every claim in the summary can be traced directly to a specific location in the source.

Limitations

Extractive summaries can feel disjointed because sentences were not written to flow together. They tend to be longer than necessary because entire sentences are included even when only part is relevant. They cannot combine information from multiple sentences into a more concise statement.

Practical applications

Extractive summarization is valuable in legal, medical, and regulatory contexts where accuracy and traceability are paramount. It is also commonly used as a first stage in hybrid approaches — extract key sentences, then use an abstractive model to rewrite them into a polished summary.