Conference Paper: Capturing user reading behaviors for personalized document summarization

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Title	Capturing user reading behaviors for personalized document summarization
Authors	Jiang, H1 Xu, S2 Lau, FCM1
Keywords	Personalized document summarization Reading preference Personal preferences Facial expressions Gaze positions Reading durations
Issue Date	2011
Publisher	Association for Computing Machinery.
Citation	The 16th international conference on Intelligent user interfaces (IUI'11), Palo Alto, CA., 13-16 February 2011. In Proceedings of 16th IUI, 2011, p. 355-358 [How to Cite?] DOI: http://dx.doi.org/10.1145/1943403.1943464
Abstract	We propose a new personalized document summarization method, which observes a user's reading behaviors, including user facial expressions, gaze positions, and reading durations, during his or her past reading activities to infer the user's personal reading preferences. Once a user's personal reading preferences are derived, our algorithm can then automatically generate document summarization in a personalized way. We compare the performance of our algorithm with that of a few peer algorithms and software packages. The result of our comparative study shows that our algorithm can produce superior personalized document summaries than those peer methods in that the automatic document summarization generated by our algorithm can better satisfy a user's personal preferences. © 2011 ACM.
ISBN	978-1-4503-0419-1
DOI	http://dx.doi.org/10.1145/1943403.1943464
References	References in Scopus

DC Field	Value
dc.contributor.author	Jiang, H
dc.contributor.author	Xu, S
dc.contributor.author	Lau, FCM
dc.date.accessioned	2012-06-26T06:32:16Z
dc.date.available	2012-06-26T06:32:16Z
dc.date.issued	2011
dc.description.abstract	We propose a new personalized document summarization method, which observes a user's reading behaviors, including user facial expressions, gaze positions, and reading durations, during his or her past reading activities to infer the user's personal reading preferences. Once a user's personal reading preferences are derived, our algorithm can then automatically generate document summarization in a personalized way. We compare the performance of our algorithm with that of a few peer algorithms and software packages. The result of our comparative study shows that our algorithm can produce superior personalized document summaries than those peer methods in that the automatic document summarization generated by our algorithm can better satisfy a user's personal preferences. © 2011 ACM.
dc.description.nature	link_to_OA_fulltext
dc.description.other	The 16th international conference on Intelligent user interfaces (IUI'11), Palo Alto, CA., 13-16 February 2011. In Proceedings of 16th IUI, 2011, p. 355-358
dc.identifier.citation	The 16th international conference on Intelligent user interfaces (IUI'11), Palo Alto, CA., 13-16 February 2011. In Proceedings of 16th IUI, 2011, p. 355-358 [How to Cite?] DOI: http://dx.doi.org/10.1145/1943403.1943464
dc.identifier.doi	http://dx.doi.org/10.1145/1943403.1943464
dc.identifier.epage	358
dc.identifier.hkuros	211555
dc.identifier.isbn	978-1-4503-0419-1
dc.identifier.scopus	eid_2-s2.0-79952774990
dc.identifier.spage	355
dc.identifier.uri	http://hdl.handle.net/10722/151998
dc.language	eng
dc.publisher	Association for Computing Machinery.
dc.publisher.place	United States
dc.relation.ispartof	Proceedings of the International Conference on Intelligent User Interfaces, IUI 2011
dc.relation.references	References in Scopus
dc.rights	Proceedings of the 16th international conference on Intelligent user interfaces, IUI'11. Copyright © Association for Computing Machinery.
dc.rights	©ACM, 2011. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in Proceedings of the 16th International Conference on Intelligent User Interfaces, 2011, http://doi.acm.org/10.1145/1943403.1943464
dc.subject	Personalized document summarization
dc.subject	Reading preference
dc.subject	Personal preferences
dc.subject	Facial expressions
dc.subject	Gaze positions
dc.subject	Reading durations
dc.title	Capturing user reading behaviors for personalized document summarization
dc.type	Conference_Paper

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Author Affiliations

The University of Hong Kong
Oak Ridge National Laboratory