Graph-based Neural Sentence Ordering

Yin, Yongjing; Song, Linfeng; Su, Jinsong; Zeng, Jiali; Zhou, Chulun; Luo, Jiebo

doi:10.24963/ijcai.2019/748

Cited by 50 publications

(45 citation statements)

References 4 publications

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“…The salience of sentences is estimated using features from graph convolutional networks (Kipf and Welling, 2016). Yin et al (2019) also propose a graph-based neural sentence ordering model, which utilizes entity linking graph to capture the global dependencies between sentences.…”

Section: Graph-based Mdsmentioning

confidence: 99%

Leveraging Graph to Improve Abstractive Multi-Document Summarization

Li¹,

Xiao²,

Liu³

et al. 2020

Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

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Graphs that capture relations between textual units have great benefits for detecting salient information from multiple documents and generating overall coherent summaries. In this paper, we develop a neural abstractive multidocument summarization (MDS) model which can leverage well-known graph representations of documents such as similarity graph and discourse graph, to more effectively process multiple input documents and produce abstractive summaries. 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. Furthermore, pre-trained language models can be easily combined with our model, which further improve the summarization performance significantly. Empirical results on the WikiSum and MultiNews dataset show that the proposed architecture brings substantial improvements over several strong baselines.

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Section: Graph-based Mdsmentioning

confidence: 99%

Leveraging Graph to Improve Abstractive Multi-Document Summarization

Li¹,

Xiao²,

Liu³

et al. 2020

Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

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show abstract

“…(2) Pointer network based models: HAN (Wang and Wan, 2019); LSTM+PtrNet (Gong et al, 2016); V-LSTM+PtrNet (Logeswaran et al, 2018); ATTOrderNet (Cui et al, 2018); SE-Graph (Yin et al, 2019); FUDecoder (Yin et al, 2020); TGCM (Oh et al, 2019).…”

Section: Baselinesmentioning

confidence: 99%

“…Previous studies (Oh et al, 2019;Yin et al, 2019) have mentioned that both the first and last sentences play crucial roles in a paragraph due to their special positions. Thus, we also report the performances of our models in correctly predicting these two sentences on arXiv and SIND datasets.…”

Section: Prediction Of First and Last Sentencesmentioning

confidence: 99%

“…On the other hand, to better capture the global coherence, pointer network (Vinyals et al, 2015) has been gradually used for the decoder of the ordering model. It is able to capture the paragraphlevel contextual information for generating an ordered sequence with the highest coherence probability (Gong et al, 2016;Logeswaran et al, 2018;Cui et al, 2018;Yin et al, 2019). Further, HAN (Wang and Wan, 2019) and TGCM (Oh et al, 2019) introduce the attention mechanism (Vaswani et al, 2017), and FUDecoder (Yin et al, 2020) proposes pairwise ordering prediction modules to enhance the traditional pointer network.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

BERT-enhanced Relational Sentence Ordering Network

Cui¹,

Li²,

Zhang³

2020

Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

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In this paper, we introduce a novel BERTenhanced Relational Sentence Ordering Network (referred to as BERSON) by leveraging BERT for capturing a better dependency relationship among sentences to enhance the coherence modeling for the entire paragraph. In particular, we develop a new Relational Pointer Decoder (referred as RPD) by incorporating the relative ordering information into the pointer network with a Deep Relational Module (referred as DRM), which utilizes BERT to exploit the deep semantic connection and relative ordering between sentences. This enables us to strengthen both local and global dependencies among sentences. Extensive evaluations are conducted on six public datasets. The experimental results demonstrate the effectiveness and promise of BERSON, showing a significant improvement over the state-of-the-art by a wide margin. * Corresponding author An unordered set of sentences Coherent paragraph 1 Dan was walking during the night.1 Dan was walking during the night. 3 They tried to steal his book bag.2 A group of thieves surrounded him. 4 A bystander noticed them.3 They tried to steal his book bag. 2 A group of thieves surrounded him. 4 A bystander noticed them. 5 But she continued to walk away.5 But she continued to walk away.

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“…Graph Convolution Networks. Recently, graph convolution network (GCN) models have increasingly attracted attention (Zhang et al, 2019), which is beneficial for graph data modeling (Yin et al, 2019). Some existing literature such as SQLto-Text (Xu et al, 2018), AMR-to-Text (Beck et al, 2018;Song et al, 2018; use GCN for generating text.…”

Section: Related Workmentioning

confidence: 99%

Summarizing Chinese Medical Answer with Graph Convolution Networks and Question-focused Dual Attention

Zhang

Chen

et al. 2020

Findings of the Association for Computational Linguistics: EMNLP 2020

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Online search engines are a popular source of medical information for users, where users can enter questions and obtain relevant answers. It is desirable to generate answer summaries for online search engines, particularly summaries that can reveal direct answers to questions. Moreover, answer summaries are expected to reveal the most relevant information in response to questions; hence, the summaries should be generated with a focus on the question, which is a challenging topic-focused summarization task. In this paper, we propose an approach that utilizes graph convolution networks and question-focused dual attention for Chinese medical answer summarization. We first organize the original long answer text into a medical concept graph with graph convolution networks to better understand the internal structure of the text and the correlation between medical concepts. Then, we introduce a question-focused dual attention mechanism to generate summaries relevant to questions. Experimental results demonstrate that the proposed model can generate more coherent and informative summaries compared with baseline models.

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Graph-based Neural Sentence Ordering

Cited by 50 publications

References 4 publications

Leveraging Graph to Improve Abstractive Multi-Document Summarization

Leveraging Graph to Improve Abstractive Multi-Document Summarization

BERT-enhanced Relational Sentence Ordering Network

Summarizing Chinese Medical Answer with Graph Convolution Networks and Question-focused Dual Attention

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