Seed and grow: augmenting statistically generated summary sentences using schematic word patterns

Stephen Wan*, Robert Dale, Mark Dras, Cécile Paris

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

5 Citations (Scopus)

Abstract

We examine the problem of content selection in statistical novel sentence generation. Our approach models the processes performed by professional editors when incorporating material from additional sentences to support some initially chosen key summary sentence, a process we refer to as Sentence Augmentation. We propose and evaluate a method called "Seed and Grow" for selecting such auxiliary information. Additionally, we argue that this can be performed using schemata, as represented by word-pair co-occurrences, and demonstrate its use in statistical summary sentence generation. Evaluation results are supportive, indicating that a schemata model significantly improves over the baseline.

Original languageEnglish
Title of host publication2008 Conference on Empirical Methods in Natural Language Processing
Subtitle of host publicationProceedings of the Conference
EditorsMirella Lapata, Hwee Tou Ng
Place of PublicationStroudsburg, PA
PublisherAssociation for Computational Linguistics (ACL)
Pages543-552
Number of pages10
Publication statusPublished - 2008
Event2008 Conference on Empirical Methods in Natural Language Processing, EMNLP 2008, Co-located with AMTA 2008 and the International Workshop on Spoken Language Translation - Honolulu, HI, United States
Duration: 25 Oct 200827 Oct 2008

Other

Other2008 Conference on Empirical Methods in Natural Language Processing, EMNLP 2008, Co-located with AMTA 2008 and the International Workshop on Spoken Language Translation
CountryUnited States
CityHonolulu, HI
Period25/10/0827/10/08

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