People have discussions in idea generation. Discussion support systems are widely studied. For example, some systems visualize discussion contents by automatically generating transcriptions. Other systems provide participants with summarized discussion content. However, few studies focus on systems that extract discussion points that have not been sufficiently discussed. Identifying such insufficient discussion points allows participants to explore them more deeply. The discussion result will be sufficient, and make participants obtain well-considered ideas from the discussion results. This paper proposes a system that extracts topics that have not been sufficiently discussed, and provides them to participants for deeper exploration. The proposed system firstly clusters the utterance texts in a discussion. Then, the system extracts topics worth exploring deeply using cluster’s density. We hypothesize that one cluster expresses one discussion topic, and a low-density cluster indicates that the topic has not been discussed enough. The proposed system extracts low-density clusters as topic aspects worth deeper exploration and visualizes them using representative utterance texts. The discussion participants refer to these extracted topics for in-depth discussion, aiming to achieve more comprehensive results. We conducted evaluation experiments with participants. The experimental results showed that discussion results were characterized by high originality, validity, and efficiency when using the proposed system.