Event　cameras　asynchronously　capture　pixel-level　intensity　changes　in　scenes　and　output　a　stream　of　events.　Compared　with　traditional　frame-based　cameras,　they　can　offer　competitive　imaging　characteristics:　low　latency,　high　dynamic　range,　and　low　power　consumption.　It　means　that　event　cameras　are　ideal　for　vision　tasks　in　dynamic　scenarios,　such　as　human　action　recognition.　The　best-performing　event-based　algorithms　convert　events　into　frame-based　representations　and　feed　them　into　existing　learning　models.　However,　generating　informative　frames　for　long-duration　event　streams　is　still　a　challenge　since　event　cameras　work　asynchronously　without　a　fixed　frame　rate.　In　this　work,　we　propose　a　novel　frame-based　representation　named　Compact　Event　Image　(CEI)　for　action　recognition.　This　representation　is　generated　by　a　self-attention　based　module　named　Event　Tubelet　Compressor　(EVTC)　in　a　learnable　way.　The　EVTC　module　adaptively　summarizes　the　long-term　dynamics　and　temporal　patterns　of　events　into　a　CEI　frame　set.　We　can　combine　EVTC　with　conventional　video　backbones　for　end-to-end　event-based　action　recognition.　We　evaluate　our　approach　on　three　benchmark　datasets,　and　experimental　results　show　it　outperforms　state-of-the-art　methods　by　a　large　margin.