With　the　development　of　text　summarization　research,　the　methods　based　on　RNN　with　the　Encoder-Decoder　model　gradually　become　the　mainstream.　However,　RNN　tends　to　forget　previous　context　information　and　leads　to　the　lack　of　original　information.　That　will　reduce　the　accuracy　of　the　generated　summarizations.　The　Transformer　model　uses　the　self-attention　mechanism　to　encode　and　decode　historical　information　so　that　it　can　achieve　better　performance　than　RNN　in　learning　context　information.　In　this　paper,　a　text　summarization　model　based　on　transformer　and　switchable　normalization　is　proposed.　The　accuracy　of　the　model　is　improved　by　optimizing　the　normalization　layer.　Compared　with　other　models,　the　new　model　has　a　great　advantage　in　understanding　words＇　semantics　and　associations.　Experimental　results　in　English　Gigaword　dataset　show　that　the　proposed　model　can　achieve　high　ROUGE　values　and　make　the　summarization　more　readable.　©　2019　IEEE.