  We propose an efficient neural framework for sentence-level discourse
analysis in accordance with Rhetorical Structure Theory (RST). Our framework
comprises a discourse segmenter to identify the elementary discourse units
(EDU) in a text, and a discourse parser that constructs a discourse tree in a
top-down fashion. Both the segmenter and the parser are based on Pointer
Networks and operate in linear time. Our segmenter yields an $F_1$ score of
95.4, and our parser achieves an $F_1$ score of 81.7 on the aggregated labeled
(relation) metric, surpassing previous approaches by a good margin and
approaching human agreement on both tasks (98.3 and 83.0 $F_1$).
