Sentence ordering driven by local and global coherence for summary generation

Authors:
Renxian Zhang
Affiliations:
The Hong Kong Polytechnic University
Venue:
HLT-SS '11 Proceedings of the ACL 2011 Student Session
Year:
2011

Citing 12
Cited 1

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Modeling local coherence: an entity-based approach

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A bottom-up approach to sentence ordering for multi-document summarization

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Measuring variability in sentence ordering for news summarization

ENLG '07 Proceedings of the Eleventh European Workshop on Natural Language Generation
Inferring strategies for sentence ordering in multidocument news summarization

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Sentence ordering with event-enriched semantics and two-layered clustering for multi-document news summarization

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Extending the entity-based coherence model with multiple ranks

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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.