We have been developing a neural network-based approach for visual information compilation. We have extended the Self-Organizing Map (SOM) model by introducing a sequencing weight function into the neuron topology, called Sequence-based SOM (SbSOM). SbSOM visualizes the dynamics of various clusters such as their generation or extinction, convergence or divergence, and merging or division. By utilizing the neuron topology and the neighborhood function of SOM, SbSOM can mitigate the problems associated to the conventional sliding-window method. We clarified a target problem class of SbSOM and confirmed the basic properties of this proposed method using a two-dimensional simulated sequential dataset. Moreover, our experiment using a dataset of real-world news articles indicates that topic transition can indeed be seen from the acquired map. Visualization of cluster sequential changes aids in the comprehension of such phenomena which come useful in various domains such as fault diagnosis and medical check-up, among others.