Papers with EDU
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. |
| Outcome: | The proposed framework achieves state-of-the-art in the Chinese Discourse Treebank dataset. |
EDU-level Extractive Summarization with Varying Summary Lengths (2023.findings-eacl)
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| Challenge: | Existing studies on extractive summarization use finer-grained elementary discourse units . few studies exploited finer grained EDUs with little analysis and justification for the extractive unit selection . |
| Approach: | They propose an extractive model with Varying summary lengths that extracts fixed top-k salient sentences from the document as a summary. |
| Outcome: | The proposed model performs better on ROUGE scores than state-of-the-art models. |
Generating EDU Extracts for Plan-Guided Summary Re-Ranking (2023.acl-long)
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| Challenge: | Existing methods to generate summary candidates for re-ranking produce redundant, and often low quality, content. |
| Approach: | They propose a method to generate candidates for re-ranking that addresses these issues by grounding each abstract on its own unique content plan and creating distinct plan-guided abstracts using a model's top beam. |
| Outcome: | The proposed method outperforms baseline decoding methods on CNN, NYT, and Xsum and shows that prompting GPT-3 to follow EDU plans outperformed sampling-based methods by 1.05 points. |
Discern: Discourse-Aware Entailment Reasoning Network for Conversational Machine Reading (2020.emnlp-main)
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Yifan Gao, Chien-Sheng Wu, Jingjing Li, Shafiq Joty, Steven C.H. Hoi, Caiming Xiong, Irwin King, Michael Lyu
| Challenge: | Document interpretation and dialog understanding are the two major challenges for conversational machine reading. |
| Approach: | They propose a discourse-aware entailment reasoning network to strengthen the connection and enhance the understanding of document and dialog. |
| Outcome: | The proposed model improves document interpretation and dialog understanding on the ShARC benchmark. |
A Unified Linear-Time Framework for Sentence-Level Discourse Parsing (P19-1)
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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. |
Automatic Pyramid Evaluation Exploiting EDU-based Extractive Reference Summaries (D18-1)
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| Challenge: | Existing methods for evaluating content are not accurate because they only confirm if the summary contains small textual fragments. |
| Approach: | They propose to transform human-made reference summaries into extractive reference sums and weight them using elementary discourse units. |
| Outcome: | The proposed method strongly correlates with manual evaluations on DUC and TAC data sets. |
Composing Elementary Discourse Units in Abstractive Summarization (2020.acl-main)
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| Challenge: | Abstractive summarization uses a single document sentence to generate a summary, but this can cause performance degradation. |
| Approach: | They propose to use elementary discourse unit (EDU) as the summarization unit to extract and group informative EDUs and then an EDU fusion model to fuse the EDU in each group into one sentence. |
| Outcome: | The proposed model can be used to combine informative EDUs into one sentence and reward selection actions. |
Cross-Document Cross-Lingual NLI via RST-Enhanced Graph Fusion and Interpretability Prediction (2025.emnlp-main)
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| Challenge: | Despite the development of many subdirections, Cross-Document Cross-Lingual NLI remains largely unexplored. |
| Approach: | They propose a novel paradigm that extends traditional NLI capabilities to multi-document, multilingual scenarios by integrating RST-enhanced graph fusion with interpretability-aware prediction. |
| Outcome: | The proposed method improves on existing models and document-level NLI to multi-document, multilingual scenarios. |