Video　summarization　aims　to　provide　a　condensed　yet　informative　version　for　original　footages　so　as　to　facilitate　content　comprehension,　browsing　and　delivery,　where　multi-modal　features　play　an　important　role　in　differentiating　individual　segments　of　a　video.　In　this　paper,　we　present　a　method　combining　both　visual　and　semantic　features.　Rather　than　utilize　domain　specific　or　heuristic　textual　features　as　semantic　features,　we　assign　semantic　concepts　to　video　segments　through　automatic　video　annotation.　Therefore,　semantic　coherence　between　accompanying　text　and　high-level　concepts　of　video　segments　is　exploited　to　characterize　the　importance　of　video　segments.　Visual　features　(e.g.　motion　and　face)　which　have　been　widely　used　in　user　attention　model-based　summarization　have　been　integrated　with　the　proposed　semantic　coherence　to　obtain　the　final　summarization.　Experiments　on　a　half-hour　sample　video　from　TRECVID　2006　dataset　have　been　conducted　to　demonstrate　that　semantic　coherence　is　very　helpful　for　video　summarization　when　being　fused　with　different　visual　features.　©　2010　Springer-Verlag.