tailieunhanh - Báo cáo khoa học: "Sentence Ordering Driven by Local and Global Coherence for Summary Generation"

In summarization, sentence ordering is conducted to enhance summary readability by accommodating text coherence. We propose a grouping-based ordering framework that integrates local and global coherence concerns. Summary sentences are grouped before ordering is applied on two levels: group-level and sentence-level. Different algorithms for grouping and ordering are discussed. The preliminary results on single-document news datasets demonstrate the advantage of our method over a widely accepted method. . | Sentence Ordering Driven by Local and Global Coherence for Summary Generation Renxian Zhang Department of Computing The Hong Kong Polytechnic University csrzhang@ Abstract In summarization sentence ordering is conducted to enhance summary readability by accommodating text coherence. We propose a grouping-based ordering framework that integrates local and global coherence concerns. Summary sentences are grouped before ordering is applied on two levels group-level and sentence-level. Different algorithms for grouping and ordering are discussed. The preliminary results on single-document news datasets demonstrate the advantage of our method over a widely accepted method. 1 Introduction and Background The canonical pipeline of text summarization consists of topic identification interpretation and summary generation Hovy 2005 . In the simple case of extraction topic identification and interpretation are conflated to sentence selection and concerned with summary informativeness. In comparison summary generation addresses summary readability and a frequently discussed generation technique is sentence ordering. It is implicitly or explicitly stated that sentence ordering for summarization is primarily driven by coherence. For example Barzilay et al. 2002 use lexical cohesion information to model local coherence. A statistical model by Lapata 2003 considers both lexical and syntactic features in calculating local coherence. More globally biased is Barzilay and Lee s 2004 HMM-based content model which models global coherence with word distribution patterns. Whilst the above models treat coherence as lexical or topical relations Barzilay and Lapata 2005 2008 explicitly model local coherence with an entity grid model trained for optimal syntactic role transitions of entities. 6 Although coherence in those works is modeled in the guise of lexical cohesion topic closeness content relatedness etc. few published works simultaneously accommodate coherence on the .

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