Improving Neural Abstractive Document Summarization with Structural Regularization (D18-1)
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| Challenge: | Recent advances in document summarization fail to capture long-term structure of documents and multi-sentence summaries, resulting in information loss and repetitions. |
| Approach: | They propose to leverage structural information of documents and multi-sentence summaries to improve document summarization performance. |
| Outcome: | The proposed model outperforms state-of-the-art models on document summarization tasks. |
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Vidhisha Balachandran, Artidoro Pagnoni, Jay Yoon Lee, Dheeraj Rajagopal, Jaime Carbonell, Yulia Tsvetkov
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| Challenge: | Existing methods for document summarization are extractive and abstractive. |
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| Challenge: | Text summarization is an important NLP problem with a wide range of applications in data-driven industries. |
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| Challenge: | Recent approaches to summarization are either selection-based extraction or generation-based abstraction. |
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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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| Challenge: | Existing neural generation approaches create multi-sentence text as a single sequence . Existing approaches create multiple sentences as if they were a sequence based on content structure . |
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Neural Text Summarization: A Critical Evaluation (D19-1)
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| Challenge: | Current approaches to text summarization use advanced attention and copying mechanisms, multi-task and multi-reward training techniques. |
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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. |
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| Challenge: | Abstractive summarization typically relies on large collections of paired articles and summaries. |
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A Discourse-Aware Attention Model for Abstractive Summarization of Long Documents (N18-2)
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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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