Patent surrogate extraction and evaluation in the context of patent mapping

Yuen Hsien Tseng*, Yeong Ming Wang, Yu I. Lin, Chi Jen Lin, Dai Wei Juang

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

42 Citations (Scopus)


Patent documents contain important research results. They are often collectively analyzed and organized in a visual way to support decision making. However, they are lengthy and rich in technical terminology, and thus require a lot of human effort for analysis. Automatic tools for assisting patent engineers or decision makers in patent analysis are in great demand. This paper describes a summarization method for patent surrogate extraction, intended to efficiently and effectively support patent mapping, which is an important subtask of patent analysis. Six patent maps were used to evaluate its relative usefulness. The experimental results confirm that the machine generated summaries do preserve more important content words than some other patent sections or even than the full patent texts when only a few terms are to be considered for classification and mapping. The implication is that if one were to determine a patent's category based on only a few terms at a quick pace, one could begin by reading the section summaries generated automatically.

Original languageEnglish
Pages (from-to)718-736
Number of pages19
JournalJournal of Information Science
Issue number6
Publication statusPublished - 2007 Dec


  • Feature extraction
  • Patent classification
  • Patent clustering
  • Summarization
  • Text mining

ASJC Scopus subject areas

  • Information Systems
  • Library and Information Sciences


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