We treat the text summarization problem as maximizing a submodular function under a budget constraint. We show, both theoretically and empirically, a modified greedy algorithm can efficiently solve the budgeted submodular maximization problem near-optimally, and we derive new approximation bounds in doing so. Experiments on DUC'04 task show that our approach is superior to the bestperforming method from the DUC'04 evaluation on ROUGE-1 scores.
REF treated the summarization task as a maximization problem of submodular functions.
Multi-document Summarization via Budgeted Maximization of Submodular Functions
Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Computer Science