Utilizing GPT to Enhance Text Summarization: A Strategy to Minimize Hallucinations

TL;DR

This paper explores using GPT to refine summaries generated by DistilBERT and T5 models, significantly reducing hallucinations and improving factual accuracy in AI-generated summaries.

Contribution

It introduces a GPT-based refining process that effectively minimizes hallucinations in both extractive and abstractive summaries, enhancing their reliability.

Findings

Refined summaries show reduced hallucinations.

Significant improvements in factual accuracy.

Enhanced reliability of AI-generated summaries.

Abstract

In this research, we uses the DistilBERT model to generate extractive summary and the T5 model to generate abstractive summaries. Also, we generate hybrid summaries by combining both DistilBERT and T5 models. Central to our research is the implementation of GPT-based refining process to minimize the common problem of hallucinations that happens in AI-generated summaries. We evaluate unrefined summaries and, after refining, we also assess refined summaries using a range of traditional and novel metrics, demonstrating marked improvements in the accuracy and reliability of the summaries. Results highlight significant improvements in reducing hallucinatory content, thereby increasing the factual integrity of the summaries.