Extractive Summarization of Long Documents by Combining Global and Local Context (D19-1)
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| Challenge: | Existing methods for extractive and abstractive summarization are far from human performance. |
| Approach: | They propose a neural single-document extractive summarization model for long documents that incorporates both the global context of the whole document and the local context. |
| Outcome: | The proposed model outperforms previous models on ROUGE-1, ROUGEE-2 and METEOR scores on two datasets of scientific papers. |
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| Challenge: | Recent years have seen success in the use of deep neural networks on text summarization, but there is no clear understanding of why they perform so well or how they might be improved. |
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Arman Cohan, Franck Dernoncourt, Doo Soon Kim, Trung Bui, Seokhwan Kim, Walter Chang, Nazli Goharian
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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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| Challenge: | Recent approaches to summarization are either selection-based extraction or generation-based abstraction. |
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SEHY: A Simple yet Effective Hybrid Model for Summarization of Long Scientific Documents (2022.findings-aacl)
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| Challenge: | Abstractive approaches to extract salient sentences from long documents are not effective due to their size. |
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Long-Span Summarization via Local Attention and Content Selection (2021.acl-long)
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| Challenge: | Transformer-based models are state-of-the-art for a wide range of natural language processing tasks, including document summarization. |
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Systematically Exploring Redundancy Reduction in Summarizing Long Documents (2020.aacl-main)
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| Challenge: | Summarization tasks are often based on importance and diversity, but there is a trade-off between importance and non-redundancy. |
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Extractive Summarization as Text Matching (2020.acl-main)
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| Challenge: | Currently, most of the neural extractive summarization systems score and extract sentences individually and model the relationship between sentences. |
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Exploiting Discourse-Level Segmentation for Extractive Summarization (D19-54)
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| Challenge: | Existing approaches to extract summarize text are based on sentences as the elementary unit, but semantic segments containing supplementary information or descriptive details are often nonessential in the generated summaries. |
| Approach: | They propose to exploit discourse-level segmentation as a finer-grained means to more precisely pinpoint the core content in a document. |
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