Papers with MultiNews
OTExtSum: Extractive Text Summarisation with Optimal Transport (2022.findings-naacl)
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| Challenge: | Extractive text summarisation aims to select salient sentences from a document to form a short yet informative summary. |
| Approach: | They propose to formulate extractive text summarisation as an Optimal Transport (OT) problem and use it to obtain an optimal summary that minimises the transportation cost to a given document. |
| Outcome: | The proposed method outperforms state-of-the-art methods and learning-based methods on multiNews, PubMed, BillSum, and CNN/DM datasets. |
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. |
Rationalizing Text Matching: Learning Sparse Alignments via Optimal Transport (2020.acl-main)
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| Challenge: | Existing models that use only rationales to explain a prediction are limited by the complexity of deep neural networks. |
| Approach: | They extend selective rationalization to text matching by using optimal transport to find a minimal cost alignment between inputs. |
| Outcome: | The proposed model achieves very sparse rationale selections with high fidelity while preserving prediction accuracy compared to strong attention baseline models. |