A Scalable Global Model for Summarization

We present an Integer Linear Program for exact inference under a maximum coverage model for automatic summarization. We com-pare our model, which operates at the sub-sentence or “concept”-level, to a sentence-level model, previously solved with an ILP. Our model scales more efficiently to larger problems because it does not require a quadratic number of variables to address re-dundancy in pairs of selected sentences. We also show how to include sentence compres-sion in the ILP formulation, which has the desirable property of performing compression and sentence selection simultaneously. The resulting system performs at least as well as the best systems participating in the recent Text Analysis Conference, as judged by a va-riety of automatic and manual content-based metrics.