Qualitative dynamics semantics allow to model large reaction networks with un- known kinetic parameters. In this poster, we present two qualitative dynamics seman- tics for reaction networks formalized into the SBGN Process Description language (SBGN-PD). These two semantics, namely the general semantics and the stories se- mantics, allow to model any SBGN-PD map into an automata network, that can then be simulated to catch the main dynamical features of the network. While the general semantics refines the standard Boolean semantics of reaction networks by taking into account all the main features of SBGN-PD, the stories semantics allows to model several molecules of a network by a unique variable, reducing in this way the size of the models. We present those two semantics and compare them on two biological network examples.