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.

Similar Papers

A Survey on Recent Advances in Keyphrase Extraction from Pre-trained Language Models (2023.findings-eacl)

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Challenge: Keyphrase extraction is a key component in Natural Language Processing (NLP) systems for selecting a set of phrases from the document that could summarize the important information discussed in the source document.
Approach: They propose to use supervised and unsupervised keyphrase extraction techniques to investigate the state-of-the-art models for keyphrase extracting.
Outcome: The proposed keyphrase extraction system can significantly accelerate the speed of retrieval and help people get first-hand information from a long document quickly and accurately.
Retrieval-Augmented Multilingual Keyphrase Generation with Retriever-Generator Iterative Training (2022.findings-naacl)

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Challenge: Existing studies on keyphrase generation on non-English languages haven’t been vastly investigated.
Approach: They propose a retrieval-augmented method for multilingual keyphrase generation that leverages keyphrase annotations in English datasets to facilitate generating keyphrases in low-resource languages.
Outcome: The proposed model outperforms baselines on non-English keyphrase generation datasets and the proposed model is scalable.
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.
Neural Keyphrase Generation via Reinforcement Learning with Adaptive Rewards (P19-1)

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Challenge: Existing generative models generate too few keyphrases, but they often generate too many . et al. (2017) propose a reinforcement learning approach for keyphrase generation .
Approach: They propose a reinforcement learning approach that encourages a model to generate sufficient keyphrases with an adaptive reward function.
Outcome: The proposed method improves state-of-the-art generative models with conventional and new evaluation methods on real-world datasets.
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.
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.
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.
A Preliminary Exploration of GANs for Keyphrase Generation (2020.emnlp-main)

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Challenge: Existing studies on extractive keyphrases have shown promising results, but the results suggest that there is room for improvement.
Approach: They propose a new keyphrase generation approach using Generative Adversarial Networks (GANs) their model produces a sequence of keyphrases and a discriminator distinguishes between human-curated and machine-generated keyphrase.
Outcome: The proposed model outperforms the state-of-the-art generative models on benchmark datasets and is comparable to the best performing extractive models.
Unsupervised Keyphrase Extraction by Learning Neural Keyphrase Set Function (2023.findings-acl)

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Challenge: Unsupervised keyphrase extraction is a task of extracting a keyphrase set that provides readers with highlevel information about the key ideas or important topics described in the document.
Approach: They propose an unsupervised keyphrase extraction task that is a document-set matching problem instead of modeling the relevance between an individual phrase and the document.
Outcome: The proposed model outperforms the state-of-the-art unsupervised keyphrase extraction baselines by a large margin.

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