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. |
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| Challenge: | Existing models for multidocument summarization have been developed that can process multiple documents in a hierarchical manner. |
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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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| Challenge: | Document structure is critical for efficient information consumption, but it is difficult to encode it efficiently into the modern Transformer architecture. |
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A Discourse-Aware Attention Model for Abstractive Summarization of Long Documents (N18-2)
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Arman Cohan, Franck Dernoncourt, Doo Soon Kim, Trung Bui, Seokhwan Kim, Walter Chang, Nazli Goharian
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Sparsity and Sentence Structure in Encoder-Decoder Attention of Summarization Systems (2021.emnlp-main)
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| Challenge: | Training and inference using large transformer models can be computationally expensive because the self-attention's time and memory grow quadratically with sequence length. |
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Hie-BART: Document Summarization with Hierarchical BART (2021.naacl-srw)
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| Challenge: | Existing document summarization models do not capture hierarchical structures of documents . proposed model incorporates multi-granularity self-attention (MG-SA) |
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Stepwise Extractive Summarization and Planning with Structured Transformers (2020.emnlp-main)
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| Challenge: | Existing approaches to extractive summarization use transformers to learn the structure of long inputs. |
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Improving Abstractive Dialogue Summarization with Hierarchical Pretraining and Topic Segment (2021.findings-emnlp)
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Unsupervised Extractive Summarization by Pre-training Hierarchical Transformers (2020.findings-emnlp)
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| Challenge: | Existing methods for document summarization use graphs and unlabeled documents . Existing models require labeled data, and it is expensive to create summarized documents. |
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Inducing Document Structure for Aspect-based Summarization (P19-1)
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| Challenge: | Abstractive summarization systems treat documents as unstructured and generate a single generic summary per document. |
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