Comparative News Summarization Using Linear Programming

Comparative News Summarization aims to highlight the commonalities and differences between two comparable news topics. In this study, we propose a novel approach to generating comparative news summaries. We formulate the task as an optimization problem of selecting proper sentences to maximize the comparativeness within the summary and the representativeness to both news topics. We consider semantic-related cross-topic concept pairs as comparative evidences, and consider topic-related concepts as representative evidences. The optimization problem is addressed by using a linear programming model. The experimental results demonstrate the effectiveness of our proposed model.