Paper: Subtree Extractive Summarization via Submodular Maximization

ACL ID P13-1101
Title Subtree Extractive Summarization via Submodular Maximization
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2013
Authors

This study proposes a text summarization model that simultaneously performs sen- tence extraction and compression. We translate the text summarization task into a problem of extracting a set of depen- dency subtrees in the document cluster. We also encode obligatory case constraints as must-link dependency constraints in or- der to guarantee the readability of the gen- erated summary. In order to handle the subtree extraction problem, we investigate a new class of submodular maximization problem, and a new algorithm that has the approximation ratio 12(1 ? e?1). Ourexperiments with the NTCIR ACLIA test collections show that our approach outper- forms a state-of-the-art algorithm.