| 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. |
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Globalizing BERT-based Transformer Architectures for Long Document Summarization (2021.eacl-main)
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| Challenge: | Existing approaches to fine-tune a large language model on downstream tasks show several limitations when the target task requires to reason with long documents. |
| Approach: | They propose a hierarchical approach where the input is divided in multiple blocks independently processed by the scaled dot-attentions and combined between the successive layers. |
| Outcome: | The proposed approach performs well on three extractive summarization corpora of scientific papers and news articles. |
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. |
| Approach: | They propose a hierarchical interactive Transformer for efficient long document modeling. |
| Outcome: | Extensive experiments on three benchmark datasets validate the efficiency and effectiveness of Hi-Transformer in long document modeling. |
Efficiently Summarizing Text and Graph Encodings of Multi-Document Clusters (2021.naacl-main)
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| Challenge: | Abstractive multi-document summarization (MDS) is a task that has seen advances with the introduction of large-scale datasets and powerful Transformer-based models. |
| Approach: | They propose an efficient graph-enhanced approach to multi-document summarization with an encoder-decoder Transformer model. |
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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. |
| Approach: | They propose to rank sentences using transformer attentions and pre-training objectives by unlabeled documents. |
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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. |
| Approach: | They propose a method for creating hierarchical summarization corpora from large, heterogeneous document collections by crowdsourcing relevant content and asking trained annotators to order the relevant information hierarchically. |
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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. |
Subtopic-driven Multi-Document Summarization (D19-1)
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| Challenge: | Experimental results show that the proposed model outperforms state-of-the-art methods on benchmark datasets. |
| Approach: | They propose a multi-document summarization model that assumes a set of documents to be summarized is on the same topic. |
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Multi-doc Hybrid Summarization via Salient Representation Learning (2023.acl-industry)
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| Challenge: | Multi-document summarization is gaining more and more attention . extractive multi-doc approaches intend to directly extract key facts from multiple sources . |
| Approach: | They propose a multi-document hybrid summarization approach that generates a human-readable summary and extracts corresponding key evidences based on multi-doc inputs. |
| Outcome: | The proposed method generates a human-readable summary and extracts key evidences based on multi-doc inputs. |
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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Long Document Summarization with Top-down and Bottom-up Inference (2023.findings-eacl)
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| Challenge: | Recent models infer latent representations of words or tokens with a transformer encoder, which is bottom-up and thus does not capture long-distance context well. |
| Approach: | They propose a method to infer latent representations of words or tokens in documents . they assume a hierarchical structure of a document where top-level captures long range dependency . |
| Outcome: | The proposed model can summarize an entire book and achieve competitive performance on a wide range of document summarization benchmarks. |