Focus Attention: Promoting Faithfulness and Diversity in Summarization (2021.acl-long)
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| Challenge: | Currently, document summarization is challenging even for humans. |
| Approach: | They propose a focus attention mechanism which encourages decoders to generate tokens that are topically similar to the input document. |
| Outcome: | The proposed method outperforms top-k and nucleus sampling methods on the BBC extreme summarization task and is more accurate than focus attention-based models. |
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| Challenge: | Abstractive summarization models require attention to reproduce the most salient information. |
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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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| Challenge: | Existing studies on faithfulness of abstractive summarization have focused on decoding strategies. |
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Focus-Constrained Attention Mechanism for CVAE-based Response Generation (2020.findings-emnlp)
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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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NEWTS: A Corpus for News Topic-Focused Summarization (2022.findings-acl)
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| Challenge: | Existing benchmarking corpora provide concordant pairs of full and abridged versions of Web, news or professional content. |
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Learning to Control Summaries with Score Ranking (2026.findings-acl)
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| Challenge: | Recent advances in summarization focus on improving summary quality across multiple dimensions, but they overlook the challenge of controlling summary generation with respect to individual dimensions. |
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Better Highlighting: Creating Sub-Sentence Summary Highlights (2020.emnlp-main)
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| Challenge: | Abstractive summarizations are considered to be less reliable because they distort the original meaning and can be confusing for readers. |
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A New Approach to Overgenerating and Scoring Abstractive Summaries (2021.naacl-main)
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| Challenge: | Abstractive summarization is a learning objective to produce system outputs that resemble reference summaries on a word-to-word basis. |
| Approach: | They propose a two-staged strategy to generate multiple variants of the target summary and score and select admissible ones according to users’ needs. |
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