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What are the different approaches to text summarization?
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Text summarization is a crucial task in Natural Language Processing (NLP) that involves creating a concise and coherent version of a larger document while keeping the key points and meaning.
The goal of summarization is to reduce the length of the text while retaining the most important information, making it easier to process large amounts of information. Summarization techniques are used in applications such as news aggregation, legal document analysis, and content recommendation systems.
There are two primary approaches to text summarization: Extractive Summarization and Abstractive Summarization. Each approach has its own methodologies and use cases.
1. Extractive Summarization:
Extractive summarization involves selecting important sentences, phrases, or sections directly from the source text and concatenating them to…
