Author(s): Rashmi

Originally published on Towards AI.

Text Summarization: Comprehensive Overview with and without RAG

Text summarization is the process of automatically condensing longer text documents into shorter versions while preserving the key information and main ideas.

This article provides an in-depth analysis of text summarization techniques, contrasting standard methods with those enhanced by Retrieval-Augmented Generation (RAG). It outlines different summarization strategies, such as extractive and abstractive summarization, along with modern approaches utilizing transformer-based models. Moreover, the article examines performance metrics for summarization, including ROUGE and BLEU scores, and discusses the trade-offs of integrating RAG models with established summarization frameworks. The results highlight the substantial improvements offered by RAG in generating high-quality summaries, albeit at the cost of increased inference time, ultimately recommending the adoption of RAG for accuracy-sensitive applications while ensuring latency remains feasible for production use.

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Published via Towards AI