sui-1: Grounded and Verifiable Long-Form Summarization

Benedikt, Jan Philipp Harries, Max Idahl, Björn Plüster

sui-1: Grounded and Verifiable Long-Form Summarization: 3 upvotes on Hugging Face Daily Papers, #25 of 27 papers on 2026-01-15. Day-by-day upvote history.

Large language models frequently generate plausible but unfaithful summaries that users cannot verify against source text, a critical limitation in compliance-sensitive domains such as government and legal analysis. We present sui-1, a 24B parameter model that produces abstractive summaries with inline citations, enabling users to trace each claim to its source sentence. Our synthetic data pipeline combines chain-of-thought prompting with multi-stage verification, generating over 22,000 high-quality training examples across five languages from diverse sources including parliamentary documents, web text, and Wikipedia. Evaluation shows sui-1 significantly outperforms all tested open-weight baselines, including models with 3x more parameters. These results demonstrate that task-specific training substantially outperforms scale alone for citation-grounded summarization. Model weights and an interactive demo are publicly available.

Paper page on Hugging Face · arXiv

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