Paper: Comparative News Summarization Using Linear Programming

ACL ID P11-2114
Title Comparative News Summarization Using Linear Programming
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2011
Authors

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 con- sider 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.