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Knowledge and Information Systems
Erschienen in: Knowledge and Information Systems 3/2014

01.03.2014 | Regular Paper

Comparative news summarization using concept-based optimization

verfasst von: Xiaojiang Huang, Xiaojun Wan, Jianguo Xiao

Erschienen in: Knowledge and Information Systems | Ausgabe 3/2014

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Abstract

Comparative news summarization aims to highlight the commonalities and differences between two comparable news topics by using human-readable sentences. The summary ought to focus on the salient comparative aspects of both topics, and at the same time, it should describe the representative properties of each topic appropriately. In this study, we propose a novel approach for generating comparative news summaries. We consider cross-topic pairs of semantic-related concepts as evidences of comparativeness and consider topic-related concepts as evidences of representativeness. The score of a summary is estimated by summing up the weights of evidences in the summary. We formalize the summarization task as an optimization problem of selecting proper sentences to maximize this score and address the problem by using a mixed integer programming model. The experimental results demonstrate the effectiveness of our proposed model.
Vorheriger Artikel An effective neural network and fuzzy time series-based hybridized model to handle forecasting problems of two factors
Nächster Artikel Maritime abnormality detection using Gaussian processes

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Fußnoten
14
The modification only alters the word filter to allow Chinese words.
 
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Metadaten
Titel
Comparative news summarization using concept-based optimization
verfasst von
Xiaojiang Huang
Xiaojun Wan
Jianguo Xiao
Publikationsdatum
01.03.2014
Verlag
Springer London
Erschienen in
Knowledge and Information Systems / Ausgabe 3/2014
Print ISSN: 0219-1377
Elektronische ISSN: 0219-3116
DOI
https://doi.org/10.1007/s10115-012-0604-8

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