To effectively avoid the influence of parse errors and take advantage of the recent advances in neural network-based approaches, we propose a model that jointly learns the discourse tree structure of the source document and a scoring function for sentence extraction.
all the parameters are updated to reproduce the correct labels and edges appearing in the training data D. λ is a parameter to control the priority of the output labels or the edges given by an RST parser.