  For extracting meaningful topics from texts, their structures should be
considered properly. In this paper, we aim to analyze structured time-series
documents such as a collection of news articles and a series of scientific
papers, wherein topics evolve along time depending on multiple topics in the
past and are also related to each other at each time. To this end, we propose a
dynamic and static topic model, which simultaneously considers the dynamic
structures of the temporal topic evolution and the static structures of the
topic hierarchy at each time. We show the results of experiments on collections
of scientific papers, in which the proposed method outperformed conventional
models. Moreover, we show an example of extracted topic structures, which we
found helpful for analyzing research activities.
