Organizing　multimodal　Web　pages　into　hot　topics　is　the　core　step　to　grasp　trends　on　the　Web.　However,　the　less-constrained　social　media　generate　noisy　user-generated　content,　which　makes　a　detected　topic　be　less　coherent　and　less　interpretable.　In　this　paper,　we　address　this　problem　by　proposing　a　coupled　Poisson　deconvolution　to　jointly　handle　topic　detection　and　topic　description.　For　the　topic　detection,　the　interestingness　of　a　topic　is　estimated　from　the　similarities　refined　by　the　description　of　topics;　for　the　topic　description,　the　interestingness　of　topics　is　leveraged　to　describe　topics.　Two　processes　cyclically　detect　interesting　topics　and　generate　the　multimodal　description　of　topics.　This　is　the　innovation　of　this　paper,　which　just　likes　killing　two　birds　with　one　stone.　Experiments　not　only　show　the　significantly　improved　accuracies　for　the　topic　detection　but　also　demonstrate　the　interpretable　descriptions　for　the　topic　description　on　two　public　data　sets.