Interactive Support System for Idea Divergence-Convergence Iteration

We propose a system to make user’s ideas more diverse as a breakthrough during creating ideas. Discussions such as brainstorming are held to stimulate user’s idea, though such composition requires much effort. For solving the problem, the proposed system presents cross-category articles related to the user’s idea as the divergence support. When the users choose one of them and the keywords shown in the articles, the sentence prototype are generated with the keywords. Afterward, the sentence prototype or its revision is input as a query of searching for the analogy-recalling articles. By iterating the processes, the user’s idea goes diversely. For obtaining the cross-category articles related to the user’s idea, the article texts and user’s idea text is input to GPT2 models to convert to vectors. The category-dependent components extracted with correlation of the category codes of the article is subtracted from the vectors to generate the category independent vectors. The cross-category but similar articles are searched based on the category independent vectors. And also, the experimental evaluation results are shown in this paper.