A Spectral Method for Unsupervised Multi-Document Summarization (2020.emnlp-main)
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| Challenge: | a spectral-based hypothesis is proposed for the unsupervised task of multi-document summarization. |
| Approach: | They propose a spectral-based hypothesis that a summary candidate's spectral impact is closely linked to its spectre. |
| Outcome: | The proposed method has a competitive result compared to state-of-the-art systems. |
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| Challenge: | Existing metrics evaluate a summary based on relevance and consistency with the source documents. |
| Approach: | They propose to measure the ability of MDS systems to handle damaging documents in their input set by lexical similarity and language model likelihood. |
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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. |
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| Challenge: | Multi-document summarization (MDS) is a task of combining multiple documents into a concise text paragraph. |
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UPER: Boosting Multi-Document Summarization with an Unsupervised Prompt-based Extractor (2022.coling-1)
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| Challenge: | Multi-Document Summarization (MDS) uses the extract-then-abstract paradigm, which extracts a relatively short meta-document and then feeds it into the deep neural networks to generate an abstract. |
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How “Multi” is Multi-Document Summarization? (2022.emnlp-main)
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| Challenge: | Multi-document summarization (MDS) aims at combining information spread across multiple documents . a single document often covers the full summary content . |
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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. |
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| Challenge: | Existing approaches to Multi-document summarization are limited due to the extremely long input length. |
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Open Domain Multi-document Summarization: A Comprehensive Study of Model Brittleness under Retrieval (2023.findings-emnlp)
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| Challenge: | Multi-document summarization (MDS) assumes a set of topic-related documents is provided as input. |
| Approach: | They formalize the task and bootstrap it using existing datasets, retrievers and summarizers. |
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Leveraging Graph to Improve Abstractive Multi-Document Summarization (2020.acl-main)
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| Challenge: | Empirical results show that our model brings substantial improvements over several strong baselines. |
| Approach: | They propose a neural abstractive multi-document summarization model which captures cross-document relations and can guide the summary generation process. |
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Multi-Document Summarization with Centroid-Based Pretraining (2023.acl-short)
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| Challenge: | In Multi-Document Summarization, the input is a set of documents, and the output is its summary. |
| Approach: | They propose a novel pretraining objective that uses the ROUGE-based centroid of each document cluster as a proxy for its summary. |
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