This lecture promotes the idea that including semantic and goal-oriented aspects in future 6G networks can produce a significant leap forward in terms of system effectiveness and sustainability. Semantic communication goes beyond the common Shannon paradigm of guaranteeing the correct reception of each single transmitted packet, irrespective of the meaning conveyed by the packet. The idea is that, whenever communication occurs to convey meaning or to accomplish a goal, what really matters is the impact that the correct reception/interpretation of a packet is going to have on the goal accomplishment. Focusing on semantic and goal-oriented aspects and possibly combining them, helps to identify the relevant information, i.e. the information strictly necessary to recover the meaning intended by the transmitter or to accomplish a goal. With this lecture we focus on the benefit of semantic compression. We present and detail a novel architecture that enables representation learning of semantic symbols for effective semantic communications. After discussing theoretical aspects and successfully design objective functions, which help learn effective semantic encoders and decoders, we present promising experimentation results for the scenario of text transmission when the sender and receiver speak different languages.