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.

本文提出一个动态和静态主题模型，能同时考虑时间上的主题演化和每个时间的主题层级结构，以此来分析结构化的时间序列文档。作者通过实验表明，该方法在科学论文集的主题提取方面优于传统模型，并展示了提取出的主题结构，以此来帮助对研究活动进行分析。