| Challenge: | Abstractive summarization models require attention to reproduce the most salient information. |
| Approach: | They propose to use local and global variances to augment the vanilla attention model to reproduce the most salient information and avoid repetitions. |
| Outcome: | The proposed attention refinement unit can reproduce the most salient information and avoid repetitions on CNN/Daily Mail dataset. |
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Arman Cohan, Franck Dernoncourt, Doo Soon Kim, Trung Bui, Seokhwan Kim, Walter Chang, Nazli Goharian
| Challenge: | Existing abstractive summarization models focus on summarizing sentences and short documents. |
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Generating Topic-Oriented Summaries Using Neural Attention (N18-1)
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| Challenge: | Existing summarization algorithms generate a single summary and are not capable of generating multiple summaries tuned to the interests of the readers. |
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Focus Attention: Promoting Faithfulness and Diversity in Summarization (2021.acl-long)
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| Challenge: | Currently, document summarization is challenging even for humans. |
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Contrastive Attention Mechanism for Abstractive Sentence Summarization (D19-1)
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| Challenge: | Existing attention mechanisms for abstractive sentence summarization are based on rule-based methods and large-scale training corpora. |
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Attention Head Masking for Inference Time Content Selection in Abstractive Summarization (2021.naacl-main)
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| Challenge: | Existing studies show that multi-heads attentions at the same layer collectively guide the summarization. |
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Highlight-Transformer: Leveraging Key Phrase Aware Attention to Improve Abstractive Multi-Document Summarization (2021.findings-acl)
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| Challenge: | Existing models do not consider key phrases in determining attention weights of self-attention . Existing work does not consider the importance of key phrases when determining weights . |
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Leveraging Locality in Abstractive Text Summarization (2022.emnlp-main)
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Yixin Liu, Ansong Ni, Linyong Nan, Budhaditya Deb, Chenguang Zhu, Ahmed Hassan Awadallah, Dragomir Radev
| Challenge: | Neural attention models have improved on many natural language processing tasks, but their quadratic memory complexity hinders their applications in long text summarization. |
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Efficient Attentions for Long Document Summarization (2021.naacl-main)
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| Challenge: | Existing models that use full attentions have quadratic computational and memory complexities, and are too costly for long documents. |
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Document Modeling with External Attention for Sentence Extraction (P18-1)
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Shashi Narayan, Ronald Cardenas, Nikos Papasarantopoulos, Shay B. Cohen, Mirella Lapata, Jiangsheng Yu, Yi Chang
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Inducing Document Structure for Aspect-based Summarization (P19-1)
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| Challenge: | Abstractive summarization systems treat documents as unstructured and generate a single generic summary per document. |
| Approach: | They propose to incorporate document structure into automatic summarization systems . they induce latent document structure and abstractive summarizing objective . |
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