| Challenge: | Abstract: Extractive summarization for long documents is challenging due to the extended structured input context. |
| Approach: | They propose a hypergraph neural network for extractive summarization by capturing cross-sentence relations. |
| Outcome: | The proposed model can capture cross-sentence relations and latent topics and keywords coreference, and section structure, and can be applied to scientific papers. |
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Multi Graph Neural Network for Extractive Long Document Summarization (2022.coling-1)
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| Challenge: | Heterogeneous Graph Neural Networks (GNN) have been proposed as an emergent approach for extracting document summarization (EDS) but there are still limitations in applying it for long documents due to the lack of inter-sentence connections. |
| Approach: | They propose to build a graph on sentence-level nodes and combine it with HeterGNN to capture the semantic information in terms of both inter and intra-sentence connections. |
| Outcome: | Experiments on two datasets show that the proposed method achieves state-of-the-art in this research field. |
HeterGraphLongSum: Heterogeneous Graph Neural Network with Passage Aggregation for Extractive Long Document Summarization (2022.coling-1)
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| Challenge: | Existing models for extractive document summarization are based on sequence-to-sequence (Seq2Sequency) but long-form document summaries using graph-based methods are still an open research issue. |
| Approach: | They propose a heterogeneous graph neural network model to improve the performance of extractive document summarization using graph-based methods. |
| Outcome: | The proposed model can achieve state-of-the-art performance without pre-trained language models. |
Hierarchical Transformers for Multi-Document Summarization (P19-1)
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| Challenge: | Existing models for multidocument summarization have been developed that can process multiple documents in a hierarchical manner. |
| Approach: | They propose a neural summarization model which can process multiple input documents and distill Transformer architecture with the ability to encode documents in a hierarchical manner. |
| Outcome: | The proposed model improves on the WikiSum dataset and can process multiple documents in a hierarchical manner. |
Heterogeneous Graph Neural Networks for Extractive Document Summarization (2020.acl-main)
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| 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. |
GraphLSS: Integrating Lexical, Structural, and Semantic Features for Long Document Extractive Summarization (2025.naacl-short)
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| Challenge: | Graph-based methods for extracting documents have been popular, but they often require external tools or additional machine learning models to define graph components. |
| Approach: | They propose a heterogeneous graph construction for extractive summarization that defines two levels of information and four types of edges without any need for auxiliary learning models. |
| Outcome: | The proposed graph construction outperforms previous graph-based models on two datasets and is available on GitHub. |
Enhancing Extractive Text Summarization with Topic-Aware Graph Neural Networks (2020.coling-main)
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| 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. |
Abstractive Summarization Guided by Latent Hierarchical Document Structure (2022.emnlp-main)
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| Challenge: | Sequential abstractive summarizations often do not capture hierarchical and inter-sentential dependencies in the summmarized document. |
| Approach: | They propose a hierarchy-aware graph neural network which captures hierarchical and inter-sentential dependencies in the summmarized document. |
| Outcome: | The proposed model improves strong sequence models such as BART with a 0.55 and 0.75 margin in ROUGE-1/2/L for CNN/DM and XSum. |
Searching for Effective Neural Extractive Summarization: What Works and What’s Next (P19-1)
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| Challenge: | Recent years have seen success in the use of deep neural networks on text summarization, but there is no clear understanding of why they perform so well or how they might be improved. |
| Approach: | They propose to use different types of model architectures to improve extractive summarization systems. |
| Outcome: | The proposed framework achieves state-of-the-art on CNN/DailyMail by a large margin based on observations and analysis. |
Extractive Summarization of Long Documents by Combining Global and Local Context (D19-1)
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| Challenge: | Existing methods for extractive and abstractive summarization are far from human performance. |
| Approach: | They propose a neural single-document extractive summarization model for long documents that incorporates both the global context of the whole document and the local context. |
| Outcome: | The proposed model outperforms previous models on ROUGE-1, ROUGEE-2 and METEOR scores on two datasets of scientific papers. |
Graph Enhanced Dual Attention Network for Document-Level Relation Extraction (2020.coling-main)
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| Challenge: | Document-level relation extraction requires inter-sentence reasoning capabilities to capture local and global contextual information for multiple relation facts. |
| Approach: | They propose to characterize the interaction between sentences and potential relation instances via a Graph Enhanced Dual Attention network (GEDA) . they also propose a simple yet effective regularizer based on the natural duality of the S2R and R2S attentions, whose weights are also supervised by the supporting evidence of relation instances during training. |
| Outcome: | The proposed model achieves competitive performance on an existing large-scale dataset while the predictions can be interpretable and easily observed. |