Extractive Summarization with SWAP-NET: Sentences and Words from Alternating Pointer Networks (P18-1)
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| Challenge: | Abstractive summarization methods use factual and grammatical errors to generate summaries. |
| Approach: | They propose a neural sequence-to-sequence model for extractive summarization called SWAP-NET . it identifies salient sentences and key words in an input document and combines them to form an extractive summary. |
| Outcome: | The proposed model outperforms state-of-the-art extractive summarization methods on large scale corpora. |
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