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. |
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| Challenge: | Embedding based methods are widely used for unsupervised keyphrase extraction tasks. |
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Unsupervised Keyphrase Extraction via Interpretable Neural Networks (2023.findings-eacl)
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Rishabh Joshi, Vidhisha Balachandran, Emily Saldanha, Maria Glenski, Svitlana Volkova, Yulia Tsvetkov
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Unsupervised Keyphrase Extraction with Multipartite Graphs (N18-2)
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| Challenge: | Recent years have witnessed a resurgence of interest in automatic keyphrase extraction. |
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Exploiting Position and Contextual Word Embeddings for Keyphrase Extraction from Scientific Papers (2021.eacl-main)
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| Challenge: | Existing methods for keyphrase extraction are either supervised or unsupervised. |
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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. |
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| Challenge: | Existing unsupervised keyphrase extraction models ignore the indicative role of the highlights in certain locations, leading to wrong keyphrases extraction. |
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AttentionRank: Unsupervised Keyphrase Extraction using Self and Cross Attentions (2021.emnlp-main)
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| Challenge: | Keyword or keyphrase extraction is to identify words or phrases presenting the main topics of a document. |
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SAMRank: Unsupervised Keyphrase Extraction using Self-Attention Map in BERT and GPT-2 (2023.emnlp-main)
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| Challenge: | Existing methods for keyphrase extraction use contextualized embeddings to capture semantic relevance between words, sentences, and documents. |
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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. |
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MDERank: A Masked Document Embedding Rank Approach for Unsupervised Keyphrase Extraction (2022.findings-acl)
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| Challenge: | Keyphrase extraction (KPE) extracts phrases in a document that provide a concise summary of the core content. |
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