Papers with MDS datasets
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. |
Multi-document Summarization with Maximal Marginal Relevance-guided Reinforcement Learning (2020.emnlp-main)
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| Challenge: | Recent studies on single-document summarization (SDS) benefit from advances in neural sequence learning, but they produce unsatisfactory results on multi-document summary (MDS). |
| Approach: | They propose a neural sequence learning method that unifies advanced neural SDS methods and statistical measures used in classical MDS. |
| Outcome: | The proposed method achieves state-of-the-art performance on benchmark MDS datasets. |
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 . |
| Approach: | They propose a measure to evaluate the degree to which a summary is "disperse" they propose to combine information from multiple documents into a single document to generate a concise summary . |
| Outcome: | The proposed measure evaluates the degree to which a summary is "disperse" the measure is applied to several popular MDS datasets and state-of-the-art systems. |