Summarization
The NLP task of condensing a longer text into a shorter version while preserving the key information and main points. Summarization can be extractive (selecting key sentences) or abstractive (generating new text).
Why It Matters
Summarization saves time and improves information digestion. It enables processing volumes of text that would be impossible to read manually.
Example
An AI summarizing a 50-page quarterly earnings report into a 5-paragraph executive summary highlighting key financial metrics, risks, and strategic initiatives.
Think of it like...
Like a newspaper editor who reads a 10-page wire story and writes a 3-paragraph article capturing the essential news — distilling without losing the core message.
Related Terms
Natural Language Processing
The branch of AI that deals with the interaction between computers and human language. NLP enables machines to read, understand, generate, and make sense of human language in a useful way.
Large Language Model
A type of AI model trained on massive amounts of text data that can understand and generate human-like text. LLMs use transformer architecture and typically have billions of parameters, enabling them to perform a wide range of language tasks.