Enhancing Extractive Text Summarization with Topic-Aware Graph Neural Networks

• URL: http://arxiv.org/abs/2010.06253v1
• Date: Tue, 13 Oct 2020 09:30:04 GMT
• Title: Enhancing Extractive Text Summarization with Topic-Aware Graph Neural Networks
• Authors: Peng Cui, Le Hu, and Yuanchao Liu
• Abstract summary: This paper proposes a graph neural network (GNN)-based extractive summarization model. Our model integrates a joint neural topic model (NTM) to discover latent topics, which can provide document-level features for sentence selection. The experimental results demonstrate that our model achieves substantially state-of-the-art results on CNN/DM and NYT datasets.
• Score: 21.379555672973975
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Text summarization aims to compress a textual document to a short summary while keeping salient information. Extractive approaches are widely used in text summarization because of their fluency and efficiency. However, most of existing extractive models hardly capture inter-sentence relationships, particularly in long documents. They also often ignore the effect of topical information on capturing important contents. To address these issues, this paper proposes a graph neural network (GNN)-based extractive summarization model, enabling to capture inter-sentence relationships efficiently via graph-structured document representation. Moreover, our model integrates a joint neural topic model (NTM) to discover latent topics, which can provide document-level features for sentence selection. The experimental results demonstrate that our model not only substantially achieves state-of-the-art results on CNN/DM and NYT datasets but also considerably outperforms existing approaches on scientific paper datasets consisting of much longer documents, indicating its better robustness in document genres and lengths. Further discussions show that topical information can help the model preselect salient contents from an entire document, which interprets its effectiveness in long document summarization.

Related papers

• Scientific Paper Extractive Summarization Enhanced by Citation Graphs [50.19266650000948]
  We focus on leveraging citation graphs to improve scientific paper extractive summarization under different settings. Preliminary results demonstrate that citation graph is helpful even in a simple unsupervised framework. Motivated by this, we propose a Graph-based Supervised Summarization model (GSS) to achieve more accurate results on the task when large-scale labeled data are available.
  arXiv  Detail & Related papers  (2022-12-08T11:53:12Z)
• A Data-driven Latent Semantic Analysis for Automatic Text Summarization using LDA Topic Modelling [0.0]
  This study presents the Latent Dirichlet Allocation (LDA) approach used to perform topic modelling. The visualisation provides an overarching view of the main topics while allowing and attributing deep meaning to the prevalence individual topic. The results suggest the terms ranked purely by considering their probability of the topic prevalence within the processed document.
  arXiv  Detail & Related papers  (2022-07-23T11:04:03Z)
• Long Document Summarization with Top-down and Bottom-up Inference [113.29319668246407]
  We propose a principled inference framework to improve summarization models on two aspects. Our framework assumes a hierarchical latent structure of a document where the top-level captures the long range dependency. We demonstrate the effectiveness of the proposed framework on a diverse set of summarization datasets.
  arXiv  Detail & Related papers  (2022-03-15T01:24:51Z)
• Topic-Aware Encoding for Extractive Summarization [15.113768658584979]
  We propose a topic-aware encoding for document summarization to deal with this issue. A neural topic model is added in the neural-based sentence-level representation learning to adequately consider the central topic information. The experimental results on three public datasets show that our model outperforms the state-of-the-art models.
  arXiv  Detail & Related papers  (2021-12-17T15:26:37Z)
• Multiplex Graph Neural Network for Extractive Text Summarization [34.185093491514394]
  Extractive text summarization aims at extracting the most representative sentences from a given document as its summary. 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.
  arXiv  Detail & Related papers  (2021-08-29T16:11:01Z)
• BASS: Boosting Abstractive Summarization with Unified Semantic Graph [49.48925904426591]
  BASS is a framework for Boosting Abstractive Summarization based on a unified Semantic graph. A graph-based encoder-decoder model is proposed to improve both the document representation and summary generation process. Empirical results show that the proposed architecture brings substantial improvements for both long-document and multi-document summarization tasks.
  arXiv  Detail & Related papers  (2021-05-25T16:20:48Z)
• Be More with Less: Hypergraph Attention Networks for Inductive Text Classification [56.98218530073927]
  Graph neural networks (GNNs) have received increasing attention in the research community and demonstrated their promising results on this canonical task. Despite the success, their performance could be largely jeopardized in practice since they are unable to capture high-order interaction between words. We propose a principled model -- hypergraph attention networks (HyperGAT) which can obtain more expressive power with less computational consumption for text representation learning.
  arXiv  Detail & Related papers  (2020-11-01T00:21:59Z)
• Leveraging Graph to Improve Abstractive Multi-Document Summarization [50.62418656177642]
  We develop a neural abstractive multi-document summarization (MDS) model which can leverage well-known graph representations of documents. Our model utilizes graphs to encode documents in order to capture cross-document relations, which is crucial to summarizing long documents. Our model can also take advantage of graphs to guide the summary generation process, which is beneficial for generating coherent and concise summaries.
  arXiv  Detail & Related papers  (2020-05-20T13:39:47Z)
• Heterogeneous Graph Neural Networks for Extractive Document Summarization [101.17980994606836]
  Cross-sentence relations are a crucial step in extractive document summarization. We present a graph-based neural network for extractive summarization (HeterSumGraph) We introduce different types of nodes into graph-based neural networks for extractive document summarization.
  arXiv  Detail & Related papers  (2020-04-26T14:38:11Z)
• Selective Attention Encoders by Syntactic Graph Convolutional Networks for Document Summarization [21.351111598564987]
  We propose a graph to connect the parsing trees from the sentences in a document and utilize the stacked graph convolutional networks (GCNs) to learn the syntactic representation for a document. The proposed GCNs based selective attention approach outperforms the baselines and achieves the state-of-the-art performance on the dataset.
  arXiv  Detail & Related papers  (2020-03-18T01:30:02Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.