Multi-document summarization for patent documents based on generative adversarial network. (30th November 2022)

• Record Type: Journal Article
• Authors: Kim, Sunhye; Yoon, Byungun
• Is Part Of: Expert systems with applications. Volume 207(2022)
• Publication Date: 2022-11-30
• Subjects: Patent summarization -- Generative adversarial network (GAN) -- Patent analysis -- Natural language processing (NLP) -- Text mining; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2022.117983
• Languages: English
• ISSNs: 0957-4174

Abstract

Abstract: Given the exponential growth of patent documents, automatic patent summarization methods to facilitate the patent analysis process are in strong demand. Recently, the development of natural language processing (NLP), text-mining, and deep learning has greatly improved the performance of text summarization models for general documents. However, existing models cannot be successfully applied to patent documents, because patent documents describing an inventive technology and using domain-specific words have many differences from general documents. To address this challenge, we propose in this study a multi-patent summarization approach based on deep learning to generate an abstractive summarization considering the characteristics of a patent. Single patent summarization and multi-patent summarization were performed through a patent-specific feature extraction process, a summarization model based on generative adversarial network (GAN), and an inference process using topic modeling. The proposed model was verified by applying it to a patent in the drone technology field. In consequence, the proposed model performed better than existing deep learning summarization models. The proposed approach enables high-quality information summary for a large number of patent documents, which can be used by R&D researchers and decision-makers. In addition, it can provide a guideline for deep learning research using patent data.