Extractive Summarization (or summary generation) works in the same way as Keyword extraction. The most relevant sentences are extracted. The algorithm selects sentences by finding the combination of words that are important or seem representative of the entire text. That’s why packages that support Summarization often also support Keyword detection. A variant is multi-document summarization.
Extractive summarization is also important for the question answering task. By collecting the most relevant documents for a particular question, a summarizer could assemble a cohesive context for the answer. The other way around is also interesting. When building training data for the QA task you have to generate relevant questions; Extractive summarization can identify important sentences where you want to have questions about.
This article is part of the project Periodic Table of NLP Tasks. Click to read more about the making of the Periodic Table and the project to systemize NLP tasks.