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Some state-of-the-art summarization systems use integer linear programming (ILP) based methods that aim to maximize the important concepts covered in the summary. These concepts are often obtained by selecting bigrams from the documents. In this paper, we improve such bigram based ILP summarization methods from different aspects. First we use syntactic information to select more important bigrams. Second, to estimate the importance of the bigrams, in addition to the internal features based on the test documents (e.g., document frequency, bigram positions), we propose to extract features by leveraging multiple external resources (such as word embedding from additional corpus, Wikipedia, Dbpedia, WordNet, SentiWordNet). The bigram weights are then trained discriminatively in a joint learning model that predicts the bigram weights and selects the summary sentences in the ILP framework at the same time. We demonstrate that our system consistently outperforms the prior ILP method on different TAC data sets, and performs competitively compared to other previously reported best results. We also conducted various analyses to show the contributions of different components.

Using External Resources and Joint Learning for Bigram Weighting in ILP-Based Multi-Document Summarization