Neural Topic Modeling by Incorporating Document Relationship Graph (2020.emnlp-main)
Copied to clipboard
| Challenge: | Graph Topic Models (GNNs) capture relationships between graph nodes via message passing . recent research has focused on topic modeling using latent Dirichlet Allocation . |
| Approach: | They propose a Graph Topic Model (GTM) that captures relationships between graph nodes via message passing. |
| Outcome: | The proposed model captures the relationships between nodes via message passing . the results demonstrate that the proposed model is effective in generating documents . |
Similar Papers
Enhancing Extractive Text Summarization with Topic-Aware Graph Neural Networks (2020.coling-main)
Copied to clipboard
| Challenge: | Existing extractive summarization models hardly capture inter-sentence relationships, especially in long documents. |
| Approach: | They propose to use a graph neural network to capture inter-sentence relationships efficiently via graph-structured document representation. |
| Outcome: | The proposed model outperforms existing models on CNN/DM and NYT datasets and significantly outperfies them on longer documents. |
Extracting Topics with Simultaneous Word Co-occurrence and Semantic Correlation Graphs: Neural Topic Modeling for Short Texts (2021.findings-emnlp)
Copied to clipboard
| Challenge: | Empirical results validate that DWGTM can generate more semantically coherent topics than baseline topic models. |
| Approach: | They develop a neural topic model which extracts topics from word co-occurrence graphs . Empirical results validate that DWGTM can generate more semantically coherent topics than baseline topic models. |
| Outcome: | Empirical results show that the proposed model can generate more coherent topics than baseline topic models. |
Topics as Entity Clusters: Entity-based Topics from Large Language Models and Graph Neural Networks (2024.lrec-main)
Copied to clipboard
| Challenge: | Topic models aim to reveal latent structures within corpus of text through term-frequency statistics over bag-of-words representations. |
| Approach: | They propose to use bimodal vector representations of entities to extract latent representations from large language models and graph neural networks trained on symbolic relations to derive the most salient aspects of these conceptual units. |
| Outcome: | The proposed approach is better suited to working with entities than state-of-the-art models. |
Text Level Graph Neural Network for Text Classification (D19-1)
Copied to clipboard
| Challenge: | Recent researches have explored graph neural network (GNN) techniques on text classification, but they are faced with the problems of fixed corpus level graph structure which don’t support online testing and high memory consumption. |
| Approach: | They propose a graph neural network model that builds graphs for each input text with global parameters sharing instead of a single graph for the whole corpus. |
| Outcome: | The proposed model outperforms existing models on several text classification datasets even with consuming less memory. |
Every Document Owns Its Structure: Inductive Text Classification via Graph Neural Networks (2020.acl-main)
Copied to clipboard
| Challenge: | Existing graph-based methods for text classification cannot capture contextual word relationships within each document nor can they produce inductive learning of new words. |
| Approach: | They propose to use Graph Neural Networks to learn the local word representations and then aggregate the word nodes as the document embeddings. |
| Outcome: | The proposed method outperforms state-of-the-art methods on four benchmark datasets. |
A Survey of Automatic Text Summarization Using Graph Neural Networks (2022.coling-1)
Copied to clipboard
| Challenge: | Abstractive ATS involves generating factually correct and fluent sentences. |
| Approach: | They provide an overview of the use of graph neural networks (GNNs) for automatic text summarization. |
| Outcome: | The proposed model is based on a set of graph neural networks (GNNs) that are used to generate a concise, correct and fluent summary of a given text. |
Heterogeneous Graph Neural Networks for Extractive Document Summarization (2020.acl-main)
Copied to clipboard
| Challenge: | Existing models capture cross-sentence relations with recurrent neural networks, but they are hard to capture sentence-level long-distance dependency. |
| Approach: | They propose a graph-based neural network for extractive summarization which contains semantic nodes apart from sentences. |
| Outcome: | The proposed graph-based neural network is the first to incorporate different types of nodes into it and perform a qualitative analysis. |
Connecting the Dots: What Graph-Based Text Representations Work Best for Text Classification using Graph Neural Networks? (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Graph Neural Networks have been used for text classification, but only in domains with limited data characteristics. |
| Approach: | They compare graph representation methods for text classification using different architectures and setups. |
| Outcome: | The proposed graph representation methods outperform other models in document comprehension tasks. |
Multiplex Graph Neural Network for Extractive Text Summarization (2021.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods for extractive text summarization do not consider multiple types of inter-sentential relationships, nor model intra-sententential relationships. |
| Approach: | They propose a novel method to combine different types of relationships among sentences and words to model sentence embedding. |
| Outcome: | The proposed model is compared with existing methods on CNN/DailyMail benchmark dataset to demonstrate its effectiveness. |
Global Context-enhanced Graph Convolutional Networks for Document-level Relation Extraction (2020.coling-main)
Copied to clipboard
| Challenge: | Existing approaches to document-level relation extraction are difficult to establish direct connections between distant entity pairs. |
| Approach: | They propose a global context-enhanced Graph Convolutional Network model which captures rich global context information of entities in a document. |
| Outcome: | The proposed model captures rich global context information of entities in a document. |