caa a22 4500 605470758 CHVBK 20210128100329.0 cr unu---uuuuu 210128e20151101xx s 000 0 eng 10.1007/s00500-014-1471-x doi (NATIONALLICENCE)springer-10.1007/s00500-014-1471-x Exploring actor-object relationships for query-focused multi-document summarization [Elektronische Daten] [Mohammadreza Valizadeh, Pavel Brazdil] Most research on multi-document summarization explores methods that generate summaries based on queries regardless of the users' preferences. We note that, different users can generate somewhat different summaries on the basis of the same source data and query. This paper presents our study on how to exploit the information regards how users summarized their texts. Models of different users can be used either separately, or in an ensemble-like fashion. Machine learning methods are explored in the construction of the individual models. However, we explore yet another hypothesis. We believe that the sentences selected into the summary should be coherent and supplement each other in their meaning. One method to model this relationship between sentences is by detecting actor-object relationship (AOR). The sentences that satisfy this relationship have their importance value enhanced. This paper combines ensemble summarizing system and AOR to generate summaries. We have evaluated this method on DUC 2006 and DUC 2007 using ROUGE measure. Experimental results show the supervised method that exploits the ensemble summarizing system combined with AOR outperforms previous models when considering performance in query-based multi-document summarization tasks. Springer-Verlag Berlin Heidelberg, 2014 User-based summarization nationallicence Actor-object relationship nationallicence Multi-document summarization nationallicence Ensemble summarizing system nationallicence Training data construction nationallicence Valizadeh Mohammadreza LIAAD INESC Tec, University of Porto, Porto, Portugal aut Brazdil Pavel LIAAD INESC Tec, University of Porto, Porto, Portugal aut Soft Computing Springer Berlin Heidelberg 19/11(2015-11-01), 3109-3121 1432-7643 19:11<3109 2015 19 500 https://doi.org/10.1007/s00500-014-1471-x text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s00500-014-1471-x text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Valizadeh Mohammadreza LIAAD INESC Tec, University of Porto, Porto, Portugal aut NATIONALLICENCE 700 1- Brazdil Pavel LIAAD INESC Tec, University of Porto, Porto, Portugal aut NATIONALLICENCE 773 0- Soft Computing Springer Berlin Heidelberg 19/11(2015-11-01), 3109-3121 1432-7643 19:11<3109 2015 19 500