SGG: Learning to Select, Guide, and Generate for Keyphrase Generation (2021.naacl-main)
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| Challenge: | Existing keyphrase generation approaches synchronously generate present and absent keyphrases without explicitly distinguishing these two categories. |
| Approach: | They propose to deal with present and absent keyphrases separately with different mechanisms by using a hierarchical neural network with a pointing-based selector and a selection-guided generator. |
| Outcome: | The proposed model outperforms baselines on four keyphrase generation tasks and shows extensibility in natural language generation tasks. |
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| Challenge: | Existing approaches to generating keyphrases for a given text are limited to extracting only the keyphrase that is directly seen in the document. |
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| Challenge: | Existing non-supervised paraphrase generation models are biased toward specific problems like question answering or image captioning. |
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Keyphrase Generation Beyond the Boundaries of Title and Abstract (2022.findings-emnlp)
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| Challenge: | Current approaches to keyphrase generation use only the title and abstract of the articles. |
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| Challenge: | Using neural models, paraphrase generation research has shifted to neural methods . a recent study focused on paraphrases, which are used in language understanding tasks . |
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An Integrated Approach for Keyphrase Generation via Exploring the Power of Retrieval and Extraction (N19-1)
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| Challenge: | Existing methods on keyphrase generation are purely extractive or generative . however, extractive methods cannot predict absent keyphrases which are not in the document. |
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Heterogeneous Graph Neural Networks for Keyphrase Generation (2021.emnlp-main)
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| Challenge: | Existing approaches for keyphrase generation generate uncontrollable and inaccurate absent keyphrases. |
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