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

W-RST: Towards a Weighted RST-style Discourse Framework