| 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. |
| Outcome: | The proposed model outperforms state-of-the-art methods on benchmark datasets. |
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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. |
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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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. |
Multi-News: A Large-Scale Multi-Document Summarization Dataset and Abstractive Hierarchical Model (P19-1)
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| Challenge: | Multi-document summarization (MDS) of news articles has been limited to datasets of a couple of hundred examples. |
| Approach: | They propose a model which integrates a traditional extractive summarization model with a standard SDS model and achieves competitive results on MDS datasets. |
| Outcome: | The proposed model achieves competitive results on large-scale datasets. |
Topic-Guided Abstractive Multi-Document Summarization (2021.findings-emnlp)
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| Challenge: | Existing studies on multi-document summarization (MDS) focus on extractive and abstractive approaches to create a fluent and concise summary for a collection of thematically related documents. |
| Approach: | They propose a novel abstractive MDS model that represents multiple documents as a heterogeneous graph and then applies a graph-to-sequence framework to generate summaries. |
| Outcome: | The proposed model outperforms state-of-the-art models on Rouge scores and human evaluation, while learning high-quality topics. |
SgSum:Transforming Multi-document Summarization into Sub-graph Selection (2021.emnlp-main)
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| Challenge: | Existing extractive multi-document summarization methods score each sentence individually and extract salient sentences one by one. |
| Approach: | They propose a novel framework for extractive multi-document summarization that selects a sub-graph as the summary instead of selecting salient sentences. |
| Outcome: | The proposed framework improves on existing methods on multi-document datasets and human evaluations show it produces more coherent and informative summaries. |
Proposition-Level Clustering for Multi-Document Summarization (2022.naacl-main)
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Ori Ernst, Avi Caciularu, Ori Shapira, Ramakanth Pasunuru, Mohit Bansal, Jacob Goldberger, Ido Dagan
| Challenge: | Existing methods focused on clustering sentences to indicate information saliency and avoid redundancy. |
| Approach: | They propose to group together sub-sentential propositions to generate a representative sentence for each cluster via text fusion. |
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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. |
Document Summarization with Latent Queries (2022.tacl-1)
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| Challenge: | Existing benchmarks for query-focused summarization are small for training large neural models. |
| Approach: | They propose a unified modeling framework for query-focused summarization . they model queries as discrete latent variables over document tokens . |
| Outcome: | The proposed framework outperforms strong comparison systems across benchmarks, query types, document settings, and target domains. |
Enhancing Multi-Document Summarization with Cross-Document Graph-based Information Extraction (2023.eacl-main)
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| Challenge: | Information extraction (IE) and summarization (summarization) are closely related, but both aims to abstract the most salient information into a generated text summary. |
| Approach: | They propose to use structured IE graphs to enhance the abstractive summarization task by using cross-document IE output to incorporate an alignment loss between IE nodes and their text spans to reduce inconsistencies. |
| Outcome: | The proposed model can generate summaries that are more factual while not losing abstractiveness. |