Large language models (LLMs) have shown amazing progress in generating text that sounds natural and informative. However, they sometimes struggle with "hallucinations," creating summaries with facts that don't match the original text. This is especially noticeable when summarizing longer documents, as LLMs tend to focus on the beginning and end of a text while potentially misrepresenting or ignoring the middle. Researchers have introduced a clever new technique called "SliSum" to combat this issue. SliSum enhances the faithfulness of LLM-generated summaries using a combination of "sliding windows" and "self-consistency." Think of it like a magnifying glass sliding across the text, ensuring the LLM pays attention to all parts equally. The sliding window divides the article into overlapping sections, creating multiple summaries of each part. These summaries are then compared, and any inconsistencies are flagged. By cleverly cross-referencing these mini-summaries, SliSum reinforces accurate details and filters out hallucinations, ensuring the final summary is both comprehensive and factually sound. This approach significantly improves the accuracy of summaries without requiring extra training or resources, and works across different LLMs and text lengths. The study showed SliSum especially shines with longer texts like scientific papers, boosting the truthfulness of AI summaries for both technical and general audiences. This advancement is crucial for using LLMs in areas where accuracy is paramount, like journalism, research, and legal fields. While challenges remain, techniques like SliSum mark a significant step towards more trustworthy AI-generated summaries.