| Challenge: | Recent keyphrase generation models are wrongly imposing a predefined order on keyphrases . a new training paradigm is proposed to concatenate keyphrase sequences in parallel . |
| Approach: | They propose a training paradigm that concatenates keyphrases in a predefined order . they propose combining a fixed set of learned control codes with a bipartite matching mechanism . |
| Outcome: | The proposed model outperforms the state-of-the-art methods on multiple benchmarks. |
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One2Set + Large Language Model: Best Partners for Keyphrase Generation (2024.emnlp-main)
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| Challenge: | Existing selection methods make redundant selections, causing poor recall and accuracy. |
| Approach: | They propose a framework to generate keyphrases from a one2set-based model and an LLM as selector. |
| Outcome: | The proposed framework surpasses state-of-the-art models in absent keyphrase prediction. |
WR-One2Set: Towards Well-Calibrated Keyphrase Generation (2022.emnlp-main)
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| Challenge: | Experimental results show that keyphrase generation has serious calibration errors . ONE2SET generates short phrases summarizing an input document . |
| Approach: | They propose a paradigm for keyphrase generation that generates short phrases summarizing an input document. |
| Outcome: | The proposed model over-estimates tokens and makes it well-calibrated on common datasets. |
Diverse Keyphrase Generation with Neural Unlikelihood Training (2020.coling-main)
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| Challenge: | Recent advances in neural natural language generation have made possible remarkable progress on the task of keyphrase generation, however, the importance of diversity in keyphrases has been largely ignored. |
| Approach: | They propose to train a sequence-to-sequence keyphrase generation model from the perspective of diversity. |
| Outcome: | The proposed model achieves large diversity gains while maintaining competitive output quality. |
Exclusive Hierarchical Decoding for Deep Keyphrase Generation (2020.acl-main)
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| Challenge: | Existing approaches to generate keyphrases ignore hierarchical compositionality of keyphrase set and generate duplicated keyphrase sets. |
| Approach: | They propose a hierarchical decoding framework that explicitly models hierarchic compositionality of a keyphrase set and either a soft or a hard exclusion mechanism to enhance the diversity of the generated keyphrases. |
| Outcome: | The proposed framework generates less duplicated and more accurate keyphrases on a set of keyphrase sets. |
Semi-Supervised Learning for Neural Keyphrase Generation (D18-1)
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| Challenge: | Existing models for keyphrase generation only use labeled data, which is limited to resource-rich domains. |
| Approach: | They propose semi-supervised keyphrase generation methods by leveraging labeled data and large-scale unlabeled samples for learning. |
| Outcome: | The proposed methods outperform state-of-the-art models trained with labeled data and large-scale unlabeled samples for learning. |
Automatic Keyphrase Generation by Incorporating Dual Copy Mechanisms in Sequence-to-Sequence Learning (2022.coling-1)
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| Challenge: | Existing models for keyphrase generation use a copy mechanism to generate keyphrases, but they do not identify key words in the source text and copy them to create more keyphrase. |
| Approach: | They propose a dual-copier keyphrase generation model that uses a sequence-to-sequence model to generate keyphrases for a piece of text. |
| Outcome: | The proposed model outperforms baseline models and achieves an obvious performance improvement. |
One Size Does Not Fit All: Generating and Evaluating Variable Number of Keyphrases (2020.acl-main)
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| Challenge: | Existing models for keyphrase generation do not provide a desideratum for the number of keyphrases in texts. |
| Approach: | They propose a recurrent generative model that generates multiple keyphrases as delimiter-separated sequences. |
| Outcome: | The proposed model outperforms baseline models on all datasets. |
Controllable Paraphrase Generation for Semantic and Lexical Similarities (2024.lrec-main)
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| Challenge: | Lexically diverse paraphrases are crucial in data augmentation because they enhance the linguistic diversity of the corpus. |
| Approach: | They propose a controllable model for semantic and lexical similarities by attaching tags to the head of the input sentence. |
| Outcome: | The proposed model can paraphrase an input sentence according to the tags specified. |
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. |
| Approach: | They propose a multi-task learning framework that jointly learns an extractive model and a generative model. |
| Outcome: | The proposed approach outperforms the state-of-the-art methods on five keyphrase generation tasks. |
Keyphrase Generation for Scientific Document Retrieval (2020.acl-main)
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| Challenge: | Sequence-to-sequence models have been used to generate keyphrases, but it is unclear whether they are reliable enough for document retrieval. |
| Approach: | They propose a framework for extrinsic evaluation that allows for a better understanding of the limitations of keyphrase generation models. |
| Outcome: | The proposed models improve retrieval performance by supplementing documents with keyphrases that are not present in the source text and generalizing models across domains. |