W-RST: Towards a Weighted RST-style Discourse Framework
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Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 13 of its 37 references that are in the map. First posted on arXiv 4 Jun 2021.
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13 of 37 references.
References · 13
- 2017Attention Is All You Needcited by 60,361
- 2014Convolutional Neural Networks for Sentence Classificationcited by 1,127
- 2016Abstractive Text Summarization Using Sequence-to-Sequence RNNs and Beyondcited by 812
- 2015Improved Semantic Representations From Tree-Structured Long Short-Term Memory Networkscited by 306
- 2017Survey of the State of the Art in Natural Language Generation: Core tasks, applications and evaluationcited by 206
- 2019Discourse-Aware Neural Extractive Text Summarizationcited by 105
- 2017Neural Discourse Structure for Text Categorizationcited by 24
- 2017Multiple Instance Learning Networks for Fine-Grained Sentiment Analysiscited by 23
- 2015Better Document-level Sentiment Analysis from RST Discourse Parsingcited by 21
- 2020Do We Really Need That Many Parameters In Transformer For Extractive Summarization? Discourse Can Help !cited by 10
- 2021Predicting Discourse Trees from Transformer-based Neural Summarizerscited by 10
- 2020MEGA RST Discourse Treebanks with Structure and Nuclearity from Scalable Distant Sentiment Supervisioncited by 8
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