Abstractive Multi-document Summarization by Partial Tree Extraction, Recombination and Linearization

Existing work for abstractive multidocument summarization utilise existing phrase structures directly extracted from input documents to generate summary sentences. These methods can suffer from lack of consistence and coherence in merging phrases. We introduce a novel approach for abstractive multidocument summarization through partial dependency tree extraction, recombination and linearization. The method entrusts the summarizer to generate its own topically coherent sequential structures from scratch for effective communication. Results on TAC 2011, DUC2004 and 2005 show that our system gives competitive results compared with state-of-the-art abstractive summarization approaches in the literature. We also achieve competitive results in linguistic quality assessed by human evaluators.