Multiplex Graph Neural Network for Extractive Text Summarization

Jing, Baoyu; You, Zeyu; Yang, Tao; Tong, Hanghang

doi:10.18653/v1/2021.emnlp-main.11

Cited by 22 publications

(9 citation statements)

References 42 publications

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“…Recent years have witnessed remarkable success in graph neural network (GNN) for natural language understanding tasks. Specifically, GNN shows superior performance on text summarization task (Feng et al 2021;Jing et al 2021), sentiment analysis task (Huang et al 2019;Liang et al 2022), dialogue understanding task (Qin et al 2021a,b) and language modeling (Meng et al 2022). Inspired by the above work, we explore GNN for better capturing relationship across multiple KBs in EToDs.…”

Section: Graph Neural Network For Nlpmentioning

confidence: 98%

Towards Complex Scenarios: Building End-to-End Task-Oriented Dialogue System across Multiple Knowledge Bases

Qin

et al. 2023

AAAI

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With the success of the sequence-to-sequence model, end-to-end task-oriented dialogue systems (EToDs) have obtained remarkable progress. However, most existing EToDs are limited to single KB settings where dialogues can be supported by a single KB, which is still far from satisfying the requirements of some complex applications (multi-KBs setting). In this work, we first empirically show that the existing single-KB EToDs fail to work on multi-KB settings that require models to reason across various KBs. To solve this issue, we take the first step to consider the multi-KBs scenario in EToDs and introduce a KB-over-KB Heterogeneous Graph Attention Network (KoK-HAN) to facilitate model to reason over multiple KBs. The core module is a triple-connection graph interaction layer that can model different granularity levels of interaction information across different KBs (i.e., intra-KB connection, inter-KB connection and dialogue-KB connection). Experimental results confirm the superiority of our model for multiple KBs reasoning.

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Section: Graph Neural Network For Nlpmentioning

confidence: 98%

Towards Complex Scenarios: Building End-to-End Task-Oriented Dialogue System across Multiple Knowledge Bases

Qin

et al. 2023

AAAI

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“…The algorithms used in previous studies include sentence compression [29], sentence fusion [30,31], and sentence revision [32]. However, encoder-decoder architectures are commonly used at present [16][17][18][19]. In the medical field, extractive summarization methods are commonly used for knowledge acquisition of clinical features, such as diseases, prescriptions, and examinations.…”

Section: Related Workmentioning

confidence: 99%

“…Because abstractive summarization can generate more flexible summaries, it has become a major approach in automatic summarization research [14][15][16][17][18][19]. However, abstractive summarization may sometimes unintentionally generate unfaithful descriptions known as hallucinations.…”

Section: Plos Digital Healthmentioning

confidence: 99%

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Is artificial intelligence capable of generating hospital discharge summaries from inpatient records?

et al. 2022

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Medical professionals have been burdened by clerical work, and artificial intelligence may efficiently support physicians by generating clinical summaries. However, whether hospital discharge summaries can be generated automatically from inpatient records stored in electronic health records remains unclear. Therefore, this study investigated the sources of information in discharge summaries. First, the discharge summaries were automatically split into fine-grained segments, such as those representing medical expressions, using a machine learning model from a previous study. Second, these segments in the discharge summaries that did not originate from inpatient records were filtered out. This was performed by calculating the n-gram overlap between inpatient records and discharge summaries. The final source origin decision was made manually. Finally, to reveal the specific sources (e.g., referral documents, prescriptions, and physician’s memory) from which the segments originated, they were manually classified by consulting medical professionals. For further and deeper analysis, this study designed and annotated clinical role labels that represent the subjectivity of the expressions and builds a machine learning model to assign them automatically. The analysis results revealed the following: First, 39% of the information in the discharge summary originated from external sources other than inpatient records. Second, patient’s past clinical records constituted 43%, and patient referral documents constituted 18% of the expressions derived from external sources. Third, 11% of the missing information was not derived from any documents. These are possibly derived from physicians’ memories or reasoning. According to these results, end-to-end summarization using machine learning is considered infeasible. Machine summarization with an assisted post-editing process is the best fit for this problem domain.

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“…Early studies, such as unsupervised LexRank [2] and TextRank [13], built similarity graphs among sentences leveraged PageRank [16] to rank them by estimating summary-worthy features of sentence importance. Recently, some works have applied graph representation learning techniques on various semantic graphs [3,5,6,17,28,32] with consideration of semantic similarity and the natural topology. However, they usually rely on external tools to construct the graphs, in which the error propagation problem is serious.…”

Section: Introductionmentioning

confidence: 99%

MuchSUM: Multi-channel Graph Neural Network for Extractive Summarization

Mao

Zhu

Liu

et al. 2022

Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval

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Recent studies of extractive text summarization have leveraged BERT for document encoding with breakthrough performance. However, when using a pre-trained BERT-based encoder, existing approaches for selecting representative sentences for text summarization are inadequate since the encoder is not explicitly trained for representing sentences. Simply providing the BERT-initialized sentences to cross-sentential graph-based neural networks (GNNs) to encode semantic features of the sentences is not ideal because doing so fail to integrate other summary-worthy features like sentence importance and positions. This paper presents MuchSUM, a better approach for extractive text summarization. MuchSUM is a multi-channel graph convolutional network designed to explicitly incorporate multiple salient summary-worthy features. Specifically, we introduce three specific graph channels to encode the node textual features, node centrality features, and node position features, respectively, under bipartite word-sentence heterogeneous graphs. Then, a cross-channel convolution operation is designed to distill the common graph representations shared by different channels. Finally, the sentence representations of each channel are fused for extractive summarization. We also investigate three weighted graphs in each channel to infuse edge features for graph-based summarization modeling. Experimental results demonstrate our model can achieve considerable performance compared with some BERT-initialized graph-based extractive summarization systems. CCS CONCEPTS• Artificial intelligence → Natural language processing; • Retrieval tasks and goals → Summarization.

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Multiplex Graph Neural Network for Extractive Text Summarization

Cited by 22 publications

References 42 publications

Towards Complex Scenarios: Building End-to-End Task-Oriented Dialogue System across Multiple Knowledge Bases

Towards Complex Scenarios: Building End-to-End Task-Oriented Dialogue System across Multiple Knowledge Bases

Is artificial intelligence capable of generating hospital discharge summaries from inpatient records?

MuchSUM: Multi-channel Graph Neural Network for Extractive Summarization

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