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1001
Grammatical Profiling for Semantic Change Detection ...
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1002
Dependency Induction Through the Lens of Visual Perception ...
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1003
Towards Syntax-Aware DialogueSummarization using Multi-task Learning ...
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1004
To be Closer: Learning to Link up Aspects with Opinions ...
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1005
Artificial Text Detection via Examining the Topology of Attention Maps ...
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1006
Enlivening Redundant Heads in Multi-head Self-attention for Machine Translation ...
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1007
On the Benefit of Syntactic Supervision for Cross-lingual Transfer in Semantic Role Labeling ...
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1008
Frequency Effects on Syntactic Rule Learning in Transformers ...
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1009
CAST: Enhancing Code Summarization with Hierarchical Splitting and Reconstruction of Abstract Syntax Trees ...
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1010
Deconstructing syntactic generalizations with minimalist grammars ...
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1011
Disentangling Representations of Text by Masking Transformers ...
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1012
Multiplex Graph Neural Network for Extractive Text Summarization ...
Abstract: Anthology paper link: https://aclanthology.org/2021.emnlp-main.11/ Abstract: Extractive text summarization aims at extracting the most representative sentences from a given document as its summary. To extract a good summary from a long text document, sentence embedding plays an important role. Recent studies have leveraged graph neural networks to capture the inter-sentential relationship (e.g., the discourse graph) to learn contextual sentence embedding. However, those approaches neither consider multiple types of inter-sentential relationships (e.g., semantic similarity & natural connection), nor model intra-sentential relationships (e.g, semantic & syntactic relationship among words). To address these problems, we propose a novel Multiplex Graph Convolutional Network (Multi-GCN) to jointly model different types of relationships among sentences and words. Based on Multi-GCN, we propose a Multiplex Graph Summarization (Multi-GraS) model for extractive text summarization. Finally, we evaluate the ...
Keyword: Computational Linguistics; Machine Learning; Machine Learning and Data Mining; Natural Language Processing; Neural Network; Text Summarization
URL: https://dx.doi.org/10.48448/r49k-tw42
https://underline.io/lecture/37444-multiplex-graph-neural-network-for-extractive-text-summarization
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1013
On the Difficulty of Translating Free-Order Case-Marking Languages ...
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1014
Data Augmentation Methods for Anaphoric Zero Pronouns ...
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1015
Revisiting Pivot-Based Paraphrase Generation: Language Is Not the Only Optional Pivot ...
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1016
Long-Range Modeling of Source Code Files with eWASH: Extended Window Access by Syntax Hierarchy ...
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1017
Counterfactual Interventions Reveal the Causal Effect of Relative Clause Representations on Agreement Prediction ...
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1018
Masked Language Modeling and the Distributional Hypothesis: Order Word Matters Pre-training for Little ...
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1019
A Data Bootstrapping Recipe for Low-Resource Multilingual Relation Classification ...
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1020
SOM-NCSCM : An Efficient Neural Chinese Sentence Compression Model Enhanced with Self-Organizing Map ...
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