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.

翻译：本研究采用DistilBERT模型生成抽取式摘要，并利用T5模型生成生成式摘要。同时，通过结合DistilBERT与T5模型构建混合摘要。研究的核心在于引入基于GPT的精炼机制，以最小化AI生成摘要中常见的幻觉问题。我们使用传统及新型评估指标对比评估未精炼摘要与精炼后的摘要，结果表明摘要的准确性与可靠性显著提升。实验结果显示，该策略能有效减少摘要中的幻觉内容，从而增强摘要的事实完整性。