We propose SUMART, a method for summarizing and compressing the volume of verbose subtitle translations. SUMART is designed for understanding translated captions (e.g., interlingual conversations via subtitle translation or when watching movies in foreign language audio and translated captions). SUMART is intended for users who want a big-picture and fast understanding of the conversation, audio, video content, and speech in a foreign language. During the training data collection, when a speaker makes a verbose statement, SUMART employs a large language model on-site to compress the volume of subtitles. This compressed data is then stored in a database for fine-tuning purposes. Later, SUMART uses data pairs from those non-compressed ASR results and compressed translated results for fine-tuning the translation model to generate more concise translations for practical uses. In practical applications, SUMART utilizes this trained model to produce concise translation results. Furthermore, as a practical application, we developed an application that allows conversations using subtitle translation in augmented reality spaces. As a pilot study, we conducted qualitative surveys using a SUMART prototype and a survey on the summarization model for SUMART. We envision the most effective use case of this system is where users need to consume a lot of information quickly (e.g., Speech, lectures, podcasts, Q&A in conferences).

本研究针对繁琐字幕翻译摘要与压缩的问题，提出了一种有效方法SUMART，旨在帮助用户快速理解外语对话和音视频内容。该方法通过大语言模型压缩冗长字幕，优化翻译模型，从而在实际应用中生成更加简明的翻译，尤其适用于需要快速获取大量信息的场合，如讲座和播客。 

SUMART：将冗长翻译总结为简明表达