SummaTRIZ : Summarization Networks for Mining Patent Contradiction

TRIZ theory is considered as a kind of innovative theory, which mainly functions in solving contradiction and aims to improve engineers creativity. It may reason on different support such as web and scientific documents. Most of the current knowledge, especially in industrial domains lies in patents. They are, nevertheless, an underused resource because of their complexity, their length and the need of domain knowledge to make use of patent. In this paper, we introduce an innovative application of deep learning to mine knowledge from patents. We show that our summarization based approach, called SummaTRIZ ¹, enables the extraction of these contradictions essential for TRIZ problem solving engine. A BERT based summarization approach is introduced to retain contradiction sentences. Our approach is experimentally evaluated on a real data set showing the performance of SummaTRIZ.