Beyond the Scientific Document: A Citation-Aware Multi-Granular Summarization Approach with Heterogeneous Graphs (2025.findings-emnlp)
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| Challenge: | Experimental results demonstrate that our model outperforms existing approaches for summarizing documents. |
| Approach: | proposed model constructs a heterogeneous graph to represent a document and its relevant external citations. |
| Outcome: | The proposed model outperforms existing models in three different scenarios. |
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SAPGraph: Structure-aware Extractive Summarization for Scientific Papers with Heterogeneous Graph (2022.aacl-main)
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Siya Qi, Lei Li, Yiyang Li, Jin Jiang, Dingxin Hu, Yuze Li, Yingqi Zhu, Yanquan Zhou, Marina Litvak, Natalia Vanetik
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| Challenge: | Automated summarization has focused on ten to twenty documents, typically news articles, but could in theory analyze hundreds of documents from a wide range of sources and provide an overview to the interested reader. |
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| Challenge: | Existing methods for document summarization use extractive and abstractive representations, but they don't take into account hierarchical structure of document clusters. |
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| Challenge: | a growing number of academic articles are shared daily, making it difficult to keep up with the latest findings. |
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| Challenge: | Current automatic summarization approaches generate abstracts, but abstracts do not show relationship between paper and references. |
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| Challenge: | Recent advances in large language models and text-aware graph learning have increased interest in reasoning over text-attributed graphs. |
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| Challenge: | Existing methods for summarizing semantic graph structure from raw text are cumbersome and inefficient for long-text documents. |
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StructSum: Summarization via Structured Representations (2021.eacl-main)
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Vidhisha Balachandran, Artidoro Pagnoni, Jay Yoon Lee, Dheeraj Rajagopal, Jaime Carbonell, Yulia Tsvetkov
| Challenge: | Abstractive summarization models overfit to training corpora, lack of transparency and layout bias . authors propose incorporating latent and explicit dependencies across sentences in source document . |
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