Concept maps are graphical representations of knowledge that show important concepts as nodes and the relationships between the concepts as links. An automatically generated concept map, depicting the overview of a specific knowledge domain or documents, can facilitate the learner's understanding of the content. Constructing a good concept map from the given text requires correct selection of concept words and identification of meaningful relationships between the concepts. We propose a new method for generating the concept map from a single document on the basis of bursts of words and the temporal relationships between the bursts. Its strength is examined in comparison with the widely used co-occurrence-based approach. The proposed method differs in that it detects where a word becomes highly topical and the places where two words share such intense periods in the discourse indicating that they form a topic together. This is more advantageous for grasping the flow of a story than the co-occurrence method that simply relates frequently accompanying words throughout the text. Also the method is less affected by high frequency words so that more meaningful relationships can be included in the top links that are limitedly accommodated in a map. For sections in a document, the burst analysis is separately performed for the entire document and for each section not to miss the background knowledge or the foreground relationships that are specific to the part. When evaluated against the human-created concept map, the proposed method shows comparable or better performance than the co-occurrence method in the recall measure and more robustness to frequent unimportant words.