Abstract
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 F1 score of
95.4, and our parser achieves an F1 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 F1).