Improving Neural Abstractive Document Summarization with Explicit Information Selection Modeling (D18-1)
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| Challenge: | Existing neural abstractive methods for document summarization are not effective for document summary. |
| Approach: | They propose to extend basic neural encoding-decoding framework with an information selection layer to explicitly model and optimize the information selection process in abstractive document summarization. |
| Outcome: | The proposed model outperforms state-of-the-art methods on document summarization tasks significantly. |
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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. |
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Arman Cohan, Franck Dernoncourt, Doo Soon Kim, Trung Bui, Seokhwan Kim, Walter Chang, Nazli Goharian
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| Challenge: | Existing methods to summarize short texts using a neural encoder-decoder are limited and expensive to obtain. |
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| Challenge: | Existing approaches to summarize text using end-to-end content selectors have had mixed success in content selection, for example copying full sentences from the source document. |
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| Challenge: | Recent work on abstractive summarization has made progress with neural encoder-decoder architectures, but these models lack explicit semantic modeling of the source document and its summary. |
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