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.

Similar Papers

Unsupervised Keyphrase Extraction via Interpretable Neural Networks (2023.findings-eacl)

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Challenge: Prior approaches for unsupervised keyphrase extraction relied on heuristic notions of phrase importance via embedding clustering or graph centrality.
Approach: They propose an approach which defines keyphrases as document phrases that are salient for predicting the topic of the document.
Outcome: The proposed method alleviates the need for ad-hoc heuristics and achieves state-of-the-art results in scientific publications and news articles.
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.
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.
Approach: They propose an unsupervised keyphrase extraction model that encodes topical information within a multipartite graph structure.
Outcome: The proposed model improves on three widely used datasets.
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.
Outcome: The proposed model outperforms the state-of-the-art unsupervised keyphrase extraction models on three benchmarks.
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.
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.
Mitigating Over-Generation for Unsupervised Keyphrase Extraction with Heterogeneous Centrality Detection (2023.emnlp-main)

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Challenge: Existing keyphrase extraction models incorrectly determine a keyphrase as a phrase but output other candidates as keyphrases because they contain the same word.
Approach: They propose a new approach that detects both implicit and explicit centrality within a heterogeneous graph as the importance score of each candidate keyphrase.
Outcome: The proposed approach outperforms state-of-the-art keyphrase extraction models on three benchmark datasets.
Hyperbolic Relevance Matching for Neural Keyphrase Extraction (2022.naacl-main)

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Challenge: Keyphrase extraction is a fundamental task in natural language processing that aims to extract a set of phrases with important information from a source document.
Approach: They propose a hyperbolic matching model to explore keyphrase extraction in hyperbolical space using word embeddings from RoBERTa to capture hierarchical syntactic and semantic structures.
Outcome: The proposed model outperforms the state-of-the-art models on six benchmark datasets and outperformed previous models.
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.
Approach: They propose a hybrid attention model to identify keyphrases from a document in an unsupervised manner.
Outcome: The proposed model is effective and robust on long and short documents.
Match More, Extract Better! Hybrid Matching Model for Open Domain Web Keyphrase Extraction (2024.findings-acl)

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Challenge: Existing models for keyphrase extraction use noisy information to filter the salient phrases from the document.
Approach: They propose a hybrid matching model that combines representation-focused and interaction-based matching modules into a unified framework for improving keyphrase extraction.
Outcome: The proposed model outperforms state-of-the-art keyphrase extraction models on the OpenKP dataset.

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