Exploring actor–object relationships for query-focused multi-document summarization

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 t...

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Bibliographic Details
Published in:Soft computing (Berlin, Germany) Germany), 2015-11, Vol.19 (11), p.3109-3121
Main Authors: Valizadeh, Mohammadreza, Brazdil, Pavel
Format: Article
Language:English
Subjects:
Algorithms
Artificial Intelligence
Boolean
Computational Intelligence
Control
Datasets
Decision trees
Documents
Engineering
Machine learning
Mathematical Logic and Foundations
Mechatronics
Methodologies and Application
Neural networks
Queries
Robotics
Summaries
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Summary: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.
ISSN:1432-7643
1433-7479
DOI:10.1007/s00500-014-1471-x