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This work studies how to transform an album to vivid and coherent stories, atask we refer to as "album storytelling". While this task can help preservememories and facilitate experience sharing, it remains an underexplored area incurrent literature. With recent advances in Large Language Models (LLMs), it isnow possible to generate lengthy, coherent text, opening up the opportunity todevelop an AI assistant for album storytelling. One natural approach is to usecaption models to describe each photo in the album, and then use LLMs tosummarize and rewrite the generated captions into an engaging story. However,we find this often results in stories containing hallucinated information thatcontradicts the images, as each generated caption ("story-agnostic") is notalways about the description related to the whole story or miss some necessaryinformation. To address these limitations, we propose a new iterative albumstorytelling pipeline. Specifically, we start with an initial story and build astory-aware caption model to refine the captions using the whole story asguidance. The polished captions are then fed into the LLMs to generate a newrefined story. This process is repeated iteratively until the story containsminimal factual errors while maintaining coherence. To evaluate our proposedpipeline, we introduce a new dataset of image collections from vlogs and a setof systematic evaluation metrics. Our results demonstrate that our methodeffectively generates more accurate and engaging stories for albums, withenhanced coherence and vividness.

Album Storytelling with Iterative Story-aware Captioning and Large Language Models