Sentence fusion via dependency graph compression
Katja Filippova, Michael Strube · 2008
We present a novel unsupervised sentence fusion method which we apply to a corpus of biographies in German.Given a group of related sentences, we align their dependency trees and build a dependency graph.Using integer linear programming we compress this graph to a new tree, which we then linearize.We use GermaNet and Wikipedia for checking semantic compatibility of co-arguments.In an evaluation with human judges our method outperforms the fusion approach of Barzilay & McKeown (2005) with respect to readability.