Communication can help multiagents learn to cooperate with each other. However, when the size of agents is large, there may exist some useless information that may impair the learning of cooperation. For this reason, this work proposes an attentional communication model that can filter out the unnecessary shared information for cooperative decision making. Inspired by the recurrent models of visual attention, this work delivers an attention unit that receives encoded local observation and action intention of an agent to determine whether it is needed to cooperate with others in its observable field. In other words, each agent called initiator, selects collaborators to form a communication group.