Rethinking Model Selection and Decoding for Keyphrase Generation with Pre-trained Sequence-to-Sequence Models (2023.emnlp-main)
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| Challenge: | Keyphrase generation is a longstanding task in NLP with widespread applications. |
| Approach: | They propose a likelihood-based decode-select algorithm for seq2seq PLMs that improves greedy search by an average of 4.7% semantic F1 across five datasets. |
| Outcome: | The proposed algorithm improves greedy search by an average of 4.7% semantic F1 across five datasets. |
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| Challenge: | a new study examines the use of encoder-only pre-trained language models in keyphrase generation (KPG) keyphrases are phrases that condense salient information of a document. |
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| Challenge: | Recent years have seen a flourishing of neural keyphrase generation (KPG) works, including the release of several large-scale datasets and a host of new models to tackle them. |
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| Challenge: | Pre-trained encoder-only and sequence-to-sequence models are computationally expensive. |
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