Automated patent document summarization for R&D intellectual property management

Abstract

In an era of rapid information expansion, people encounter huge amounts of intellectual property (IP) such as patents in digital format. These documents are usually too numerous to be fully utilized in R&D for new product designs. Therefore, efficient and effective ways of acquiring, organizing and presenting IPs (e.g., patent documents) have become very important for enterprises. In this paper, we propose a patent document summarization system using an integrated approach of key-phrase recognition and significant information density. Significant information density or information mass is calculated based on the summation of key-phrases, their relevant phrases, title phrases, domain-specific phrases, indicator phrases and topic sentences, divided by the total number of phrases in a paragraph or a document. External text mining game, compression ratio and retention ratio are used in system experiment and evaluation. This research enables enterprises to organize knowledge and intellectual assets efficiently and to peruse IP documents effectively.