Machine learning in Evolving Connectionist Text Summarizer

Over the past half century, the problem of text summarization has been addressed from many different perspectives, in various domains and using various paradigms. This paper intends to investigate machine learning for the text summarization system, taking into account of exciting new developments in adaptive evolving systems. Evolving processes, through both individual development and population evolution, inexorably led the human race to our supreme intelligence and our superior position in the animal kingdom. In this paper, we consider the system of an Automatic Text Summarization as an evolving system which learns incrementally through experience in the environment. This paper highlights the machine learning process for an Evolving Connectionist Text Summarizer ECTS, which is a Computational Intelligence (CI) system that operate continuously in the time and adapt their structure and functionality through a continuous interaction with the environment and with other systems.