Poetry, as a profound and creative form of human expression, presents unique challenges in interpretation and summarization due to its reliance on figurative language, symbolism, and deeper meanings. Building upon the PoemSum dataset, which introduced the task of poem summarization, we extend its scope by exploring multimodal applications. Specifically, we implement and fine-tune two state-of-the-art abstractive summarization models—BART and T5—to generate concise and meaningful interpretations of poems, focusing on figurative summarization that captures metaphorical and symbolic elements inherent in poetic language. These summaries are then transformed into visual representations using two diffusion-based generative models: Stable Diffusion for high-quality image generation. Our approach evaluates the effectiveness of abstractive summarization models in capturing the essence of poetry and demonstrates how diffusion models can translate abstract poetic themes into visually compelling images. Evaluation results show that the BART model outperforms T5 in summarization, achieving a high ROUGE score of 41.90 and a BERTScore of 85.22. For image generation, the Inception Score (IS) of 7.63 ± 0.62 reflects high visual quality and diversity, while the CLIP (Contrastive Language-Image Pre-training) Score of 29.48 indicates strong alignment between textual summaries and generated images.