Papers with Multi-Document Summarization
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. |
| Outcome: | The proposed model is better or comparable to state-of-the-art models. |
Improving Multi-Document Summarization through Referenced Flexible Extraction with Credit-Awareness (2022.naacl-main)
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| Challenge: | Existing approaches to Multi-document summarization are limited due to the extremely long input length. |
| Approach: | They propose an extract-then-abstract Transformer framework to overcome the problem . they leverage pre-trained language models to construct hierarchical extractors and abstractors . |
| Outcome: | The proposed framework outperforms baseline models with comparable model sizes and achieves the best results on the Multi-News, Multi-XScience, and WikiCatSum corpora. |
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. |
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. |
| Approach: | They propose to use pre-trained language models to calculate document and keyword’s perplexity to boost other metrics for evaluating a document’s salience. |
| Outcome: | The proposed method can be applied as a plug-in to boost other metrics for evaluating a document’s salience, thus improving the subsequent abstract generation. |
Topic-Guided Reinforcement Learning with LLMs for Enhancing Multi-Document Summarization (2025.findings-emnlp)
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| Challenge: | Large Language Models (LLMs) have shown impressive results in single-document summarization, but their performance on MDS still leaves room for improvement. |
| Approach: | They propose a topic-guided reinforcement learning approach to improve content selection in MDS . explicit prompting models with topic labels enhances the informativeness, they show . |
| Outcome: | The proposed method outperforms baselines on multi-News and multi-XScience datasets. |