Abstract
According to the report of World Intellectual Property Organization (WIPO), patent documents are the only type of documents that can totally disclose core techniques, and there are 90% to 95% R&D achievements in commercialization comparing to 5% to 10% disclosure rate of other types of documents (e.g. technical reports, and journal articles). By the investigation of WIPO, as long as a company can make the best use of patent information, it can save R&D costs by 40% and shorten the R&D time by 60%. As a consequence, patent information has been playing an important role in the era of knowledge-based economy. However, the numbers of patent documents are increasing dramatically, and most researchers cannot process, organize and understand them with an effective manner. Moreover, it is increasingly difficult for researchers to fully understand patent documents with a lot of technical and legal vocabularies in the context. In this paper, we propose an ontology-based key-phrase recognition technology for the construction of an automated summarization system. In addition, the patents of Power Hand Tool and Chemical Mechanical Polishing are used to verify the effectiveness of proposed summarization system. First, the system extracts domain key words by using a pre-defined ontology, and uses TF-IDF method to extract high frequency terms. Second, a clustering algorithm, K-Mean, is adopted, and the content with similar concept will be gathered together. Third, the candidate paragraphs are picked up from each cluster by using key words and phrases to measure every paragraph importance in each cluster. Finally, the candidate paragraphs are combined with template that is defied in advance, and the text summary is generated at this stage. In addition, the system will mark, annotate and highlight the nodes of ontology tree that are corresponding to words in the document, and produce a visualized feature of summary.