Abstractive summarization (AS) is a natural language processing (NLP) task that generates a concise and coherent summary of a given document by rephrasing or paraphrasing the content, capturing the essential information rather than directly extracting sentences or phrases from the source text. AS is utilized in multiple mission-critical downstream tasks across various domains, including healthcare and law. Nevertheless, the existing state-of-the-art AS models are based on black-box deep learning models, such as Transformers, and they cannot explain why specific facts were included in the summary while others were omitted. This chapter proposes a novel framework for explaining which facts have been excluded from a summary by a given AS model and the rationale behind the selections. First, the effectiveness of existing feature attribution methods in explaining the decisions of AS models is analyzed. Then, a new framework, Fact Omission Explanation (FOE), is introduced, which utilizes a feature attribution method to analyze the fact-selection process of a given AS model and generate a linguistic explanation of the facts excluded and their respective reasons. The proposed framework was assessed using the Pub-Med dataset, PEGASUS (a state-of-the-art AS model), and quantitative metrics. The results demonstrate that FOE produces relevant and comprehensive explanations that elucidate the omission of key facts by AS models, thereby enhancing the trustworthiness of summarization systems.