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. |
| Approach: | They propose an unsupervised algorithm that exploits contextual word embeddings and positional information to create a biased PageRank. |
| Outcome: | The proposed algorithm outperforms previous approaches and strong baselines on five benchmark datasets. |
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| Challenge: | Keyphrase extraction is a fundamental task in natural language processing that facilitates mapping of documents to a set of representative phrases. |
| Approach: | They propose an unsupervised technique that leverages phrase embeddings for ranking keyphrases extracted from scientific articles using theme-weighted PageRank. |
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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. |
| Approach: | They propose an unsupervised keyphrase extraction method that ranks candidates by similarity between embeddings of source document and masked document. |
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Improving Unsupervised Keyphrase Extraction using Background Knowledge (L18-1)
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| Challenge: | Existing methods of keyphrase extraction are supervised and unsupervised . Topical PageRank uses topical information to extract the top topics of a document . |
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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
| Challenge: | Prior approaches for unsupervised keyphrase extraction relied on heuristic notions of phrase importance via embedding clustering or graph centrality. |
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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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HyperRank: Hyperbolic Ranking Model for Unsupervised Keyphrase Extraction (2023.emnlp-main)
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| Challenge: | Existing unsupervised keyphrase extraction models overlook latent hierarchical structures when extracting keyphrases. |
| Approach: | They propose a new ranking model that models global and local contexts to estimate the importance of each candidate keyphrase within the hyperbolic space. |
| Outcome: | The proposed model outperforms state-of-the-art models in keyphrase extraction tasks. |
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. |
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SaSAKE: Syntax and Semantics Aware Keyphrase Extraction from Research Papers (2020.coling-main)
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| Challenge: | Syntax and semantics Aware Keyphrase Extraction (SaSAKE) is a novel method for extracting keyphrases from research papers. |
| Approach: | They propose a method to automatically extract keyphrases from research papers using a transformer architecture and graph encoders to incorporate syntactic and semantic dependency graphs. |
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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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Improving Embedding-based Unsupervised Keyphrase Extraction by Incorporating Structural Information (2023.findings-acl)
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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. |
| Approach: | They propose a Highlight-Guided Unsupervised Keyphrase Extraction model that models phrase-document relevance via the highlights of documents and calculates cross-phrase relevance between all candidate phrases. |
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