GraphTalk: Slide-Anchored Topic Graphs with Supporting Document Evidence for Presentation Narration

Generating spoken narration (a talk track) for an existing slide deck requires following the slides' topical structure while grounding each slide in evidence from long supporting documents. We present GraphTalk, a training-free pipeline built around an explicit, interpretable structured planning layer that replaces the flat, high-volume source context used by prior approaches. GraphTalk induces atomic topics from the slides, enriches them with retrieved document evidence, ranks them over a similarity graph with a PageRank-seeded variant of DivRank, and allocates them to slides by capacity-constrained matching in a shared embedding space. On two benchmarks (M3AV academic lectures and SEC EDGAR financial filings), two generation models, and a dual-ladder of deterministic and LLM-as-judge metrics, its central result is the strongest document-grounded slide coverage and relevance, holding across deterministic metrics and three LLM judges, at a token budget comparable to the strongest baselines. A small human study supports the coverage advantage. Automatic judges are unreliable for supporting-document usage, where our human study is more trustworthy and also favours GraphTalk. We validate judge reliability with multi-judge agreement, an image-grounded judge, and perturbation tests.