Global revision in summarisation

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.