| Challenge: | Existing document summarization models do not capture hierarchical structures of documents . proposed model incorporates multi-granularity self-attention (MG-SA) |
| Approach: | They propose a new abstractive document summarization model, hierarchical BART . the proposed model captures hierarchically structured sentences in the BART model . |
| Outcome: | The proposed model outperforms baseline models and improves performance on CNN/Daily Mail dataset. |
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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. |
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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. |
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Beyond Generic Summarization: A Multi-faceted Hierarchical Summarization Corpus of Large Heterogeneous Data (L18-1)
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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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Does the structure of textual content have an impact on language models for automatic summarization? (2024.acl-srw)
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| Challenge: | Existing models for automatic summarization of long sequences suffer from context limitation. |
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Hierarchical Attention Graph for Scientific Document Summarization in Global and Local Level (2024.findings-naacl)
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| Challenge: | Existing methods for document summarization focus on one type of relation, neglecting the simultaneous effective modeling of both relations. |
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HIBERT: Document Level Pre-training of Hierarchical Bidirectional Transformers for Document Summarization (P19-1)
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| Challenge: | Neural extractive summarization models employ hierarchical encoders with inaccurate sentence-level labels. |
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Hi-Transformer: Hierarchical Interactive Transformer for Efficient and Effective Long Document Modeling (2021.acl-short)
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| Challenge: | Existing approaches to model long documents are difficult due to the quadratic complexity of text length. |
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HIBRIDS: Attention with Hierarchical Biases for Structure-aware Long Document Summarization (2022.acl-long)
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Fact-level Extractive Summarization with Hierarchical Graph Mask on BERT (2020.coling-main)
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| Challenge: | Existing extractive summarization models generate summaries by selecting salient sentences, but there is a gap between the human-written gold summary and oracle sentence labels. |
| Approach: | They propose to extract fact-level semantic units for better extractive summarization by incorporating a hierarchical structure into the model and incorporate it with BERT using a Hierarchical graph mask. |
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A Mixed Hierarchical Attention Based Encoder-Decoder Approach for Standard Table Summarization (N18-2)
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| Challenge: | Structured data summarization involves generation of summaries from structured input data. |
| Approach: | They propose a hierarchical attention-based encoder-decoder model which leverages the structure in addition to the content of the tables. |
| Outcome: | The proposed model improves on the weathergov dataset by 30% over the current state-of-the-art. |