sui-1: Grounded and Verifiable Long-Form Summarization
This work addresses the lack of verifiability in summaries generated by large language models, which poses significant trustworthiness risks in compliance-sensitive domains such as government and legal applications. To mitigate this issue, the authors propose a 24-billion-parameter verifiable summarization model that, for the first time, enables fine-grained citation tracing in multilingual long-document summarization—each summary span is explicitly linked to its source sentence in the original text. The model is trained using chain-of-thought prompting, a multi-stage automatic verification mechanism, and a synthetic data pipeline, drawing from diverse multilingual sources including parliamentary proceedings, web pages, and Wikipedia across five languages. Experimental results demonstrate that the proposed approach substantially outperforms all open-source baselines, including models with three times its parameter count. Model weights and an interactive demo are publicly released.