Organizing　interesting　webpages　into　hot　topics　is　one　of　key　steps　to　understand　the　trends　of　multimodal　web　data.　A　state-of-the-art　solution　is　firstly　to　organize　webpages　into　a　large　volume　of　multi-granularity　topic　candidates;　hot　topics　are　further　identified　by　estimating　their　interestingness.　However,　these　topic　candidates　contain　a　large　number　of　fragments　of　hot　topics　due　to　both　the　inefficient　feature　representations　and　the　unsupervised　topic　generation.　This　paper　proposes　a　bundling-refining　approach　to　mine　more　complete　hot　topics　from　fragments.　Concretely,　the　bundling　step　organizes　the　fragment　topics　into　coarse　topics;　next,　the　refining　step　proposes　a　submodular-based　method　to　refine　coarse　topics　in　a　scalable　approach.　The　propose　unconventional　method　is　simple,　yet　powerful　by　leveraging　submodular　optimization,　our　approach　outperforms　the　traditional　ranking　methods　which　involve　the　careful　design　and　complex　steps.　Extensive　experiments　demonstrate　that　the　proposed　approach　surpasses　the　state-of-the-art　method　(i.e.　,　latent　Poisson　deconvolution　Pang　et　al.　(2016))　20%　accuracy　and　10%　one　on　two　public　data　sets,　respectively.