This paper explores two linguistically motivated restrictions on the set of words used for topic tracking on newspaper articles: named entities and headline words. We assume that named entities is one of the linguistic features for topic tracking, since both topic and event are related to a specific place and time in a story. The basic idea to use headline words for the tracking task is that headline is a compact representation of the original story, which helps people to quickly understand the most important information contained in a story. Headline words are automatically generated using headline generation technique. The method was tested on the Mainichi Shimbun Newspaper in Japanese, and the results of topic tracking show that the system works well even for a small number of positive training data.