As generative models continue to evolve, the automated generation of scientific posters from research papers has advanced rapidly. However, its evaluation almost exclusively focuses on whether the poster layout is visually well-organized and whether it covers the paper’s content. We argue that these approaches overlook a prior question: whether the selection of content for the poster is sound. A poster can be complete and well-formed, yet distort findings, break internal coherence, or spend its limited space on duplicated information. We introduce CoSeCh (Content Selection Check), a suite of automatic rule-based, LLM-assisted diagnostics that probe the selected content on four distinct dimensions of quality: faithfulness (are the qualifiers that bound a claim preserved?), coherence (do selected units remain interpretable once their dependencies are removed?), recoverability (can the paper’s core claims be reconstructed from the selection?), and efficiency (is the budget spent on distinct content?). Applying CoSeCh to 5 poster generators ranging from a random selection baseline to state-of-the-art works like P2P and Paper2Poster, we find that content selection quality is strongly generator-dependent and that generators fail along different axes. Some generators preserve the qualifiers for the claims better than human authors, while others’ selection is no more coherent than random choice. Existing coverage-focused evaluation metrics like PaperQuiz cannot detect these failures. The four axes are near-independent, showing that no single coverage measure can capture them.