Ichiro Kobayashi

This paper describes a method of multi-document summarization with evolutionary computation. In automatic document sum-marization, the method to make a summary by finding the best combination of important sentences in target documents is popular ap-proach. To find the best combination of sen-tences, explicit solution techniques such as in-teger linear programming, branch and bound method, and so on are usually adopted. How-ever, there is a problem with them in terms of calculation efficiency. So, we apply evolution-ary computation, especially differential evo-lution which is regarded as a method having a good feature in terms of calculation cost to obtain a reasonable quasi-optimum solution in real time, to the problem of combinatorial op-timization of important sentences. Moreover, we consider latent topics in deciding the im-portance of a sentence, and define three fitness functions to compare the results. As a result, we have confirmed that our proposed meth-ods reduced the calculation time necessary to make a summary considerably, although pre-cision is more worse than the method with an explicit solution technique.