Comparative news summarization using concept-based optimization

• Published in: Knowledge and information systems Vol. 38; no. 3; pp. 691 - 716
• Main Authors: Huang, Xiaojiang, Wan, Xiaojun, Xiao, Jianguo
• Format: Journal Article
• Language: English
• Published: London Springer London 01.03.2014; Springer; Springer Nature B.V
• Subjects: Analysis; Applied sciences; Artificial intelligence; Commonality; Comparative analysis; Computer Science; Computer science; control theory; systems; Data mining; Data Mining and Knowledge Discovery; Database Management; Exact sciences and technology; Information Storage and Retrieval; Information systems; Information Systems and Communication Service; Information Systems Applications (incl.Internet); Information systems. Data bases; Integer programming; IT in Business; Linear programming; Linguistics; Memory organisation. Data processing; Mining accidents & safety; News; Optimization; Regular Paper; Semantics; Sentences; Software; Speech and sound recognition and synthesis. Linguistics; Studies; Summaries; Tasks; Mixed integer programming; Comparative text mining; Multi-document summarization; Comparative news summarization; Hierarchical classification; Abstract; Text; Data mining; Conceptual analysis; Legibility; Modeling; Optimization; Experimental result; Semantics; Linguistics; Sentence; Pattern extraction; Mathematical programming
• ISSN: 0219-1377; 0219-3116
• DOI: 10.1007/s10115-012-0604-8

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.