Member-only story
Unlocking the Power of Text Summarization with Large Language Models (LLMs)
In our digital era, where data is ubiquitous and overwhelmingly voluminous, the ability to efficiently condense large texts into digestible summaries is invaluable. This is where Large Language Models (LLMs) like Ollama come into play, offering sophisticated capabilities in text summarization. This blog post delves deep into the mechanics and applications of text summarization using LLMs, showcased through a detailed Python implementation.
Understanding Text Summarization
Text summarization is the process of succinctly reducing a longer text to its most important points, essentially capturing the essence in fewer words. There are two primary approaches:
- Extractive Summarization: This technique extracts key portions of the text, stitching them together to form a cohesive summary without altering the original wording.
- Abstractive Summarization: More complex, this method involves generating new phrases and sentences that summarize the original text, which may not appear verbatim in the source material.
The provided example employs abstractive summarization using an LLM, harnessing its advanced capabilities to comprehend and reformulate text meaningfully.
