  In this work, we develop a neural network based model which leverages
dependency parsing to capture cross-positional dependencies and grammatical
structures. With the help of linguistic signals, sentence-level relations can
be correctly captured, thus improving news documents summarization performance.
Empirical studies demonstrate that this simple but effective method outperforms
existing works on the benchmark dataset. Extensive analyses examine different
settings and configurations of the proposed model which provide a good
reference to the community.
