Text Summarization Framework Using Machine Learning

Abstract

A crucial problem in natural language processing is text summarization, which calls for models to provide succinct, logical summaries while maintaining the accuracy and consistency of the original data. However, the practical applications of modern abstractive summarizing techniques are limited by the ongoing compromise between diversity and consistency of facts.By adding a new factuality-guided module to the diffusion process, our work suggests a Factuality-Guided Diffusion-Based Abstractive Summarization Model. Intermediate representations are ensured to closely reflect the original text by the factuality module. and the model repeatedly denoizes random noise to provide summaries. Without requiring the fundamental summarization model to be retrained, this approach guarantees variety and factual consistency. The suggested approach outperforms current methods in terms of factual correctness, according to experimental results on two benchmark datasets. The results demonstrate that this approach is a viable way to get over the limitations of the existing abstractive summarizing techniques.