Improving the Markov Chain Approach for Generating Text Used for Ideation Tasks
Keywords:
Computational Creativity, Ideation, Markov Chain, Text Generation, Artificial Intelligence, Innovation, Crowd SourcingAbstract
The increased demand for text-related solutions from generation, learning, classification, and several other tasks has motivated the use of different techniques and tools of Artificial intelligence. Creative text ideas have been sought after for innovation, problem solving, and improvements, and coming up with them can be a daunting task. In this work, an idea generation system based on improvements to the Markov chain approach using a corpus of text is presented. First, a web system was created to collect solutions from people on a case study problem. They were required to make submissions based on purpose and mechanism, with examples to guide them. Next, the solution text from 200 participants was clustered based on similarity measures into groups, and abstractive summaries of the respective groups were computed. The Markov chain model was then used for the generation of new text from the submitted text corpus, and the most similar Markov chain-generated text was compared with each clustered group’s abstractive summary using a similarity measure and returned as an idea result. Finally, a pipeline to execute all the components of the system at once was developed. The result was sent for human evaluation based on the metrics of quality, novelty, and variety and compared with output from a Generative Pretrained Transformer system using the same text corpus, and this work’s system performed better.
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