A Unified Model for Extractive and Abstractive Summarization using Inconsistency Loss (P18-1)
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| Challenge: | extractive models can obtain sentence-level attention with high ROUGE scores but less readable. abstractive models generate novel words and phrases not copied from the source text. |
| Approach: | They propose to combine extractive and abstractive models to achieve a unified model that generates readable paragraphs with word-level attention. |
| Outcome: | The proposed model achieves state-of-the-art ROUGE scores while being the most informative and readable summarization on the CNN/Daily Mail dataset in a solid human evaluation. |
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| Challenge: | Abstractive summarization systems have traditionally been fragmented, limiting the benefits of compatible models. |
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Multi-Granularity Interaction Network for Extractive and Abstractive Multi-Document Summarization (2020.acl-main)
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| Challenge: | Existing methods for document summarization use extractive and abstractive representations, but they don't take into account hierarchical structure of document clusters. |
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| Challenge: | We present a method to produce abstractive summaries of documents that exceed several thousand words . we compare transformer based methods to extractive methods, but extractive models score higher . |
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Arman Cohan, Franck Dernoncourt, Doo Soon Kim, Trung Bui, Seokhwan Kim, Walter Chang, Nazli Goharian
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| Challenge: | Recent studies show that document summarization systems are abstractive . authors suggest that automated summarizing systems could be improved . |
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| Challenge: | Existing extractive systems lack gold training signals, thereby hindering learning of extractive models. |
| Approach: | They propose to use text generators to train extractive summarizers by approximating outputs of abstractive summaries. |
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To Point or Not to Point: Understanding How Abstractive Summarizers Paraphrase Text (2021.findings-acl)
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| Challenge: | Abstractive summarization models have seen great improvements in recent years, but there is limited understanding of the strategies different models employ and how they relate their understanding of language. |
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Summary Level Training of Sentence Rewriting for Abstractive Summarization (D19-54)
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| Challenge: | Existing models rely on sentence-level rewards or suboptimal labels to achieve summary-level ROUGE scores. |
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The Summary Loop: Learning to Write Abstractive Summaries Without Examples (2020.acl-main)
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| Challenge: | Unsupervised abstractive summarization is important for news headlines and research papers . a novel method that encourages the inclusion of key terms from the original document into the summary is presented . |
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Don’t Give Me the Details, Just the Summary! Topic-Aware Convolutional Neural Networks for Extreme Summarization (D18-1)
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| Challenge: | Existing approaches to summarize documents are not extractive and require an abstractive approach. |
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