Subtree Extractive Summarization via Submodular Maximization

This study proposes a text summarization model that simultaneously performs sentence extraction and compression. We translate the text summarization task into a problem of extracting a set of dependency subtrees in the document cluster. We also encode obligatory case constraints as must-link dependency constraints in order to guarantee the readability of the generated 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). Our experiments with the NTCIR ACLIA test collections show that our approach outperforms a state-of-the-art algorithm.