Personalized Document Summarization Using Pseudo Relevance Feedback and Semantic Feature

Abstract

This paper proposes a new automatic personalized document summarization using pseudo relevance feedback (PRF) and semantic features to extract meaningful sentences from retrieval documents in the Internet. The proposed method uses generic summarization based on the semantic features of non-negative matrix factorization to extract sentences that well reflect the major topics of the searched documents. In addition, this method reduces the semantic gap between the low level of summarizing search results and the high level of user’s perception; the method uses query-based summarization depending on PRF and semantic features. The method improves the quality of personalized document summarization because the sentences most relevant to the given query are extracted efficiently by using a combination of generic and query-based summarization. The experimental results demonstrate that the proposed method achieves a better document summarization performance than do other methods.

Keywords:

• Non-negative matrix factorization
• Personalized document summarization
• Pseudo relevance feedback
• Semantic features

Additional information

Notes on contributors

Sun Park

Sun Park is a research professor at Research Faculty Institute of Information Science and Engineering Research, Mokpo National University, Korea. He received the Ph.D degree in Computer and Information Engineering from Inha University in 2007, the M.S. degree in Information and Communication Engineering from Hannam University in 2001, and the B.S. degree in Computer Engineering from Jeonju University in 1996. Prior to becoming a researcher at Mokpo National University, he has worked as a postdoctoral at Chonbuk National University, and professor in Dept. of Computer Engineering, Honam University, Korea. His research interests include Data Mining, Information Retrieval, and Information Summarization. E-mail: [email protected]

Byung Rae Cha

Byung Rae Cha is a research professor at school of information and communication, GIST, Korea. He received the Ph.D. degree in computer engineering from National Mokpo University in 2004 and the M.S. degree in computer engineering from Honam University in 1997. Prior to becoming a research professor at GIST, he has worked as a research professor in department of information and communication eng., Chosun University, and professor in department of computer engineering, Honam University, Korea. His research interests include Computer Security of IDS and P2P, Neural Networks Learning, Mobile-OTP, Future Internet, and Cloud Computing. E-mail: [email protected]

JangWoo Kwon

JangWoo Kwon received the B.S degree in electronic Eng. from INHA University in 1990, the M.E. and Ph.D. degree in electronic engineering from INHA University in 1992 and 1996, respectively. In 1992 he was a visiting Researcher at Department of Biomedical Engineering of Tokyo University, Tokyo, Japan. From 1996 to 1998 he was a deputy director of Korea Industrial Property Office (KIPO) where his responsibility was to examine patents. From 1998 to 2009 he was an Associate Professor of Department of Computer Engineering at Tongmyoung University, Pusan, Korea. He had been a Dean of Research Institute for Information Eng. Tech. at Tongmyoung University from 2002 to 2006. From 2010 to 2012 he was an Associate Professor of Department of Computer Eng. at Kyungwon University, Kyeong-gi Province, Korea. Since 2006, he has been a Director of Human Resource Development Division of National IT industry promotion agency of Korea. Currently, his research area is in sensor networks and human computer interaction using biomedical signals. For the last 20 years he has been working in biomedical signal analysis and its recognition using artificial intelligence. E-mail: [email protected]