| Challenge: | Creating keyphrases that are likely to be words absent from the given document is challenging . |
| Approach: | They propose novel keyphrase generation tasks that augment missing context by adding keyphrases to documents. |
| Outcome: | The proposed keyphrase generation task outperforms the state-of-the-art in two keyphrase tasks. |
Similar Papers
Data Augmentation for Low-Resource Keyphrase Generation (2023.findings-acl)
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| Challenge: | Existing works on keyphrase generation rely on large-scale annotated datasets, which are not easy to acquire. |
| Approach: | They propose to use full text to improve keyphrase generation in resource-constrained domains by using the full text of the articles to augment their methods. |
| Outcome: | The proposed methods improve both present and absent keyphrase generation on three datasets and show that they are cost-effective. |
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. |
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. |
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. |
| Approach: | They propose to integrate full text and semantically similar articles to generate keyphrases from a dataset that includes the full text of the articles along with the title and abstract. |
| Outcome: | The proposed model can generate keyphrases that are present or absent from the text. |
Keyphrase Generation: A Text Summarization Struggle (N19-1)
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| Challenge: | Existing methods for keyphrase generation are unable to produce valuable terms that do not appear in the text. |
| Approach: | They propose to consider the keyphrase string as an abstractive summary of the title and the abstract. |
| Outcome: | The proposed method can generate better keyphrases than the existing methods or the unsupervised ones. |
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. |
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. |
| Approach: | They propose a graph-based method that captures explicit knowledge from related references. |
| Outcome: | The proposed model improves on baseline keyphrase generation models on multiple benchmarks. |
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. |
Topic-Aware Neural Keyphrase Generation for Social Media Language (P19-1)
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| Challenge: | Existing methods to extract words from source posts to form keyphrases do not exploit latent topics. |
| Approach: | They propose a sequence-to-sequence-based neural keyphrase generation framework . it allows absent keyphrases to be created, and it allows joint modeling of latent topic representations . |
| Outcome: | The proposed model outperforms extraction and generation models without exploiting latent topics. |
AGRank: Augmented Graph-based Unsupervised Keyphrase Extraction (2022.aacl-main)
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| Challenge: | Unsupervised keyphrase extraction (UKE) is highly anticipated because no labeled data is needed to train a model. |
| Approach: | They propose an augmented graph-based unsupervised model to identify keyphrases from a document by integrating graph and deep learning methods. |
| Outcome: | The proposed model is effective and robust for long and short documents. |