KeyGames: A Game Theoretic Approach to Automatic Keyphrase Extraction

Saxena, Arnav; Mangal, Mudit; Jain, Goonjan

doi:10.18653/v1/2020.coling-main.184

Cited by 20 publications

(11 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…EmbedRank [57], Doc2Vec EmbedRank [57], KeyGames [58], Joint Modeling [59], KeyBert [60], Yake [61], Rake [62], etc. are implemented on user posts, and the performance is measured using Precision, Recall, and F1Score.…”

Section: Unsupervised Keyword Extraction Algorithms Including Sifrank...mentioning

confidence: 99%

Rumi: An Intelligent Agent Enhancing Learning Management Systems Using Machine Learning Techniques

Jafari¹,

Zhao²,

Jafari³

2022

JSEA

View full text Add to dashboard Cite

The dramatic improvement of information and communication technology (ICT) has made an evolution in learning management systems (LMS). The rapid growth in LMSs has caused users to demand more advanced, automated, and intelligent services. This paper discusses how Artificial Intelligence and Machine Learning techniques are adopted to fulfill users' needs in a social learning management system named "CourseNetworking". The paper explains how machine learning contributed to developing an intelligent agent called "Rumi" as a personal assistant in CourseNetworking platform to add personalization, gamification, and more dynamics to the system. This paper aims to introduce machine learning to traditional learning platforms and guide the developers working in LMS field to benefit from advanced technologies in learning platforms by offering customized services.

show abstract

Section: Unsupervised Keyword Extraction Algorithms Including Sifrank...mentioning

confidence: 99%

Rumi: An Intelligent Agent Enhancing Learning Management Systems Using Machine Learning Techniques

Jafari¹,

Zhao²,

Jafari³

2022

JSEA

View full text Add to dashboard Cite

show abstract

“…Florescu and Caragea (2017) tries to score the candidate phrases as the aggregation of its words score, but over-generation errors will happen. Saxena, Mangal, and Jain (2020) transforms keyphrase extraction into classification problem using evolutionary game theory. The major common drawback of these keyphrase extraction methods is that they can only extract keyphrases that already appear in the source text, but many relevant keyphrases may not appear in the text as consecutive spans.…”

Section: Keyphrase Extraction and Generationmentioning

confidence: 99%

Unsupervised Deep Keyphrase Generation

Shen

Wang

Meng

et al. 2022

AAAI

View full text Add to dashboard Cite

Keyphrase generation aims to summarize long documents with a collection of salient phrases. Deep neural models have demonstrated remarkable success in this task, with the capability of predicting keyphrases that are even absent from a document. However, such abstractiveness is acquired at the expense of a substantial amount of annotated data. In this paper, we present a novel method for keyphrase generation, AutoKeyGen, without the supervision of any annotated doc-keyphrase pairs. Motivated by the observation that an absent keyphrase in a document may appear in other places, in whole or in part, we construct a phrase bank by pooling all phrases extracted from a corpus. With this phrase bank, we assign phrase candidates to new documents by a simple partial matching algorithm, and then we rank these candidates by their relevance to the document from both lexical and semantic perspectives. Moreover, we bootstrap a deep generative model using these top-ranked pseudo keyphrases to produce more absent candidates. Extensive experiments demonstrate that AutoKeyGen outperforms all unsupervised baselines and can even beat a strong supervised method in certain cases.

show abstract

“…SIFRank (Sun et al, 2020) improves EmbedRank with contextualized embedding from a pre-trained language model. KeyGames (Saxena et al, 2020) creatively introduces game theoretic approach into automatic keyphrase extraction.…”

Section: Comparison Models and Implementation Detailsmentioning

confidence: 99%

Unsupervised Keyphrase Extraction by Jointly Modeling Local and Global Context

Liang¹,

Wu²,

Li³

et al. 2021

Preprint

View full text Add to dashboard Cite

Embedding based methods are widely used for unsupervised keyphrase extraction (UKE) tasks. Generally, these methods simply calculate similarities between phrase embeddings and document embedding, which is insufficient to capture different context for a more effective UKE model. In this paper, we propose a novel method for UKE, where local and global contexts are jointly modeled. From a global view, we calculate the similarity between a certain phrase and the whole document in the vector space as transitional embedding based models do. In terms of the local view, we first build a graph structure based on the document where phrases are regarded as vertices and the edges are similarities between vertices. Then, we proposed a new centrality computation method to capture local salient information based on the graph structure. Finally, we further combine the modeling of global and local context for ranking. We evaluate our models on three public benchmarks (Inspec, DUC 2001, SemEval 2010 and compare with existing state-of-the-art models. The results show that our model outperforms most models while generalizing better on input documents with different domains and length. Additional ablation study shows that both the local and global information is crucial for unsupervised keyphrase extraction tasks.

show abstract

KeyGames: A Game Theoretic Approach to Automatic Keyphrase Extraction

Cited by 20 publications

References 23 publications

Rumi: An Intelligent Agent Enhancing Learning Management Systems Using Machine Learning Techniques

Rumi: An Intelligent Agent Enhancing Learning Management Systems Using Machine Learning Techniques

Unsupervised Deep Keyphrase Generation

Unsupervised Keyphrase Extraction by Jointly Modeling Local and Global Context

Contact Info

Product

Resources

About