  Aiming for a better integration of data-driven and linguistically-inspired
approaches, we explore whether RST Nuclearity, assigning a binary assessment of
importance between text segments, can be replaced by automatically generated,
real-valued scores, in what we call a Weighted-RST framework. In particular, we
find that weighted discourse trees from auxiliary tasks can benefit key NLP
downstream applications, compared to nuclearity-centered approaches. We further
show that real-valued importance distributions partially and interestingly
align with the assessment and uncertainty of human annotators.
