Comparing the two approximation strategies shows a clear performance advantage for DP+LP over DP+LP→MST: the latter approach entails slower inference due to the overhead of running the Chu-Liu Edmonds algorithm at every dual update, and furthermore, the error introduced by approximating an integral solution re-sults in a significant decrease in dependency recall.
furthermore, much recent work has focused on the challenge of joint sentence extraction and compression, also known as compressive summarization Berg-Kirkpatrick et al., 2011;Almeida and Martins, 2013;Li et al., 2013;Qian and Liu, 2013), in which questions of efficiency are paramount due to the larger problems involved; however, these approaches largely restrict compression to pruning parse trees, thereby imposing a dependency on parser performance.