Attributable by Construction: Claim-Anchored Provenance for Multi-Document Summarization
Large language models produce fluent multi-document summaries, but their attributions are typically coarse---whole documents or passages---and generated post hoc, leaving each statement hard to verify. We argue that attribution should be a structural property of generation rather than a downstream prediction. We present CAMS, a Claim-Anchored Multi-document Summarization framework that decomposes every source document into atomic claims whose provenance is resolved deterministically from verbatim quotes to token spans, clusters equivalent claims across documents while flagging inter-source conflicts, selects a support-aware and salient subset, and rewrites it so that every summary sentenc...
| Authors | Shuo Guan |
|---|---|
| Primary category | cs.CL |
| Published | 22 June 2026 |
| Last updated at source | 11 September 2026 |
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