| Challenge: | Existing discourse segmenters rely on complicated hand-crafted features and are not practical in actual use. |
| Approach: | They propose an end-to-end neural segmenter based on BiLSTM-CRF framework that can segment texts fast and accurately using a large corpus. |
| Outcome: | The proposed model is significantly faster than previous methods while achieving state-of-the-art performance on the RST-DT corpus. |
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| Challenge: | a new neural framework for sentence-level discourse analysis is proposed . a discourse segmenter and a parser are based on pointer networks and operate in linear time . |
| Approach: | They propose a neural framework for sentence-level discourse analysis in accordance with Rhetorical Structure Theory . they use a discourse segmenter and a parser to construct a discursive tree in a top-down fashion . |
| Outcome: | The proposed framework surpasses previous approaches on both tasks and human agreement on both. |
Unleashing the Power of Neural Discourse Parsers - A Context and Structure Aware Approach Using Large Scale Pretraining (2020.coling-main)
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| Challenge: | Discourse parsing is an important upstream task within the area of Natural Language Processing (NLP) . |
| Approach: | They propose a discourse parser that incorporates recent contextual language models to improve the performance of RST-based discourse parses. |
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Improving Context Modeling in Neural Topic Segmentation (2020.aacl-main)
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| Challenge: | Recent work favors highly effective neural supervised approaches for topic segmentation but current neural solutions are limited in how they model context. |
| Approach: | They propose to enhance a hierarchical attention biLSTM network-based topic segmenter to better model context by adding a coherence-related auxiliary task and restricted self-attention. |
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Exploiting Discourse-Level Segmentation for Extractive Summarization (D19-54)
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| Challenge: | Existing approaches to extract summarize text are based on sentences as the elementary unit, but semantic segments containing supplementary information or descriptive details are often nonessential in the generated summaries. |
| Approach: | They propose to exploit discourse-level segmentation as a finer-grained means to more precisely pinpoint the core content in a document. |
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Discourse Representation Parsing for Sentences and Documents (P19-1)
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| Challenge: | Experimental results show that our model outperforms competitive baselines by a wide margin. |
| Approach: | They propose a neural model which parses discourse structures of arbitrary length and granularity. |
| Outcome: | The proposed model outperforms baseline models on sentence- and document-level benchmarks. |
A Language Model-based Generative Classifier for Sentence-level Discourse Parsing (2021.emnlp-main)
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| Challenge: | Existing methods to consider textual coherence are limited in labeled data. |
| Approach: | They propose a language model-based generative classifier that uses labels as input and embeds labels into their representations. |
| Outcome: | The proposed classifier achieves state-of-the-art in discourse segmentation and relation F1 scores with gold boundaries and automatically segmented boundaries. |
Text Segmentation by Cross Segment Attention (2020.emnlp-main)
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| Challenge: | Document and discourse segmentation are two fundamental NLP tasks pertaining to breaking up text into constituents. |
| Approach: | They propose three transformer-based NLP models that break up text into constituents and compare them to previous approaches. |
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Top-down Discourse Parsing via Sequence Labelling (2021.eacl-main)
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| Challenge: | Discourse analysis is a systematic way to understand how texts are segmented hierarchically into discourse units. |
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A Simple and Strong Baseline for End-to-End Neural RST-style Discourse Parsing (2022.findings-emnlp)
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| Challenge: | Existing discourse parsing methods need a strong baseline for reporting reliable experimental results. |
| Approach: | They integrate existing parsing strategies with transformer-based pre-trained language models to provide a strong baseline for reporting reliable experimental results. |
| Outcome: | The proposed model outperforms the current best model using DeBERTa. |
A Unified RvNN Framework for End-to-End Chinese Discourse Parsing (C18-2)
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| Challenge: | Existing work on Chinese discourse parser relies on external packages to extract linguistic features from free text. |
| Approach: | They propose an end-to-end Chinese discourse parser based on recursive neural network to jointly model the subtasks including elementary discourse unit segmentation, tree structure construction, center labeling, and sense labeling. |
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