Multi-document abstractive summarization using chunk-graph and recurrent neural network

Abstract

Automatic multi-document abstractive summarization system is used to summarize several documents into a short one with generated new sentences. Many of them are based on word-graph and ILP method, and lots of sentences are ignored because of the heavy computation load. To reduce computation and generate readable and informative summaries, we propose a novel abstractive multi-document summarization system based on chunk-graph (CG) and recurrent neural network language model (RNNLM). In our approach, A CG which is based on word-graph is constructed to organize all information in a sentence cluster, CG can reduce the size of graph and keep more semantic information than word-graph. We use beam search and character-level RNNLM to generate readable and informative summaries from the CG for each sentence cluster, RNNLM is a better model to evaluate sentence linguistic quality than n-gram language model. Experimental results show that our proposed system outperforms all baseline systems and reach the state-of-art systems, and the system with CG can generate better summaries than that with ordinary word-graph.