Global revision in summarisation

generating novel sentences with Prim's algorithm

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contribution


In abstract-like summarisation, extracted sentences containing key content are often revised to improve the coherence of the overall summary. In this work, we consider the task of Global Revision, in which a key sentence is revised and supplemented with additional content from the original document. Specifically, this task comprises two subtasks: selecting content; and grammatically ordering content, the focus of this paper. Using statistical dependency models, we search for a Maximal Spanning (Dependency) Tree that structures recycled words and phrases to form a novel sentence. Combining a modified version of Prim’s algorithm with a four-gram language model, we evaluated our system on a sentence regeneration task obtaining Bleu scores of .30, a statistically significant improvement above the baseline.
Original languageEnglish
Title of host publicationPACLING '07
Subtitle of host publicationproceedings of the conference Pacific Association for Computational Linguistics ; 19-21 September 2007 University of Melbourne, Melbourne, Australia
Place of PublicationMelbourne, Australia
PublisherPacific Association for Computational Linguistics
Number of pages10
Publication statusPublished - 2007
EventConference of the Pacific Association for Computational Linguistics (10th : 2007) - Melbourne
Duration: 19 Sep 200721 Sep 2007


ConferenceConference of the Pacific Association for Computational Linguistics (10th : 2007)

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