Online Context-Aware Task Assignment in Mobile Crowdsourcing via Adaptive Discretization

Elahi, Sepehr; Nika, Andi; Tekin, Cem

doi:10.1109/tnse.2022.3207418

Cited by 5 publications

(3 citation statements)

References 38 publications

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“…For Sentence Ranking, TextRank uses the PageRank model on sentence-centric graph-based model. It combines outgoing links from sentence node with the similarity between two sentences as mentioned in equation (2).…”

Section: Textrankmentioning

confidence: 99%

“…Most of the generated information falls under the wider umbrella of Text Mining (TM) approaches, where the data, or information is gathered from devices, and is distributed to different users for consumption 1 . Another field is crowdsourcing 2 , which includes involvement of public opinions, work distribution, and community shares from a large group of persons. Mostly, the information is generated by mobile devices and the content is user-generated, with significant amount of textual information.…”

Section: Introductionmentioning

confidence: 99%

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GETS : Sentence Scoring Scheme in Graph-based Extractive Text Summarization for Text Mining Applications

Bhattacharya

Verma

Shir

et al. 2023

Preprint

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Recently, there is an exponential influx of textual data in big data applications, which necessitates the requirement of text mining tools for analysis of data. In Text Mining applications (TM), Text Summarization (TS) has emerged as an emergent field in Natural Language Processing (NLP). Mostly, in TS, abstractive approaches are presented which build complex models, and thus, a shift is envisioned towards graph-based extractive text summarization models. Such models allow review and feedback analysis of a service or product, and have the benefits of being less complex, flexible, and require low computational resources. This makes them an effective fit for modern text mining based big data and Internet-of-Things (IoT) applications. Thus, in the proposed work, we present a scheme, GETS, which exploits a graph-based model to establish relations between words and sentences based on statistical operations. In the scheme, a post processing phase is presented which uses sentence clustering based on graph preparation. To make the scheme scalable fit for real world applications, we use the Apache Spark environment for parallel execution of graph-based operations. In experimental setup, the Recall-oriented Understudying Gisting Evaluation (ROUGE) parameters is used to evaluate the proposed graph based model with a comparative analysis with ROUGE 1,2,L measures. Comparative analysis is done based on clustered and non-clustered approaches. The obtained results renders the scheme effective as a backend of Artificial Intelligence (AI) models in crowdsourcing applications and decision-analytics models.

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Section: Textrankmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

GETS : Sentence Scoring Scheme in Graph-based Extractive Text Summarization for Text Mining Applications

Bhattacharya

Verma

Shir

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Most of the generated information falls under the wider umbrella of text mining (TM) schemes, where the data or information are gathered from devices and distributed to different users for consumption [1]. Another field is crowdsourcing [2], which includes the involvement of public opinions, work distribution, and community shares from a large group of persons. Mostly, the information is generated by mobile devices and the content is user-generated, with a significant amount of textual information.…”

Section: Introductionmentioning

confidence: 99%

Graph-Based Extractive Text Summarization Sentence Scoring Scheme for Big Data Applications

Verma,

Bhargav,

Bhavsar

et al. 2023

Information

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The recent advancements in big data and natural language processing (NLP) have necessitated proficient text mining (TM) schemes that can interpret and analyze voluminous textual data. Text summarization (TS) acts as an essential pillar within recommendation engines. Despite the prevalent use of abstractive techniques in TS, an anticipated shift towards a graph-based extractive TS (ETS) scheme is becoming apparent. The models, although simpler and less resource-intensive, are key in assessing reviews and feedback on products or services. Nonetheless, current methodologies have not fully resolved concerns surrounding complexity, adaptability, and computational demands. Thus, we propose our scheme, GETS, utilizing a graph-based model to forge connections among words and sentences through statistical procedures. The structure encompasses a post-processing stage that includes graph-based sentence clustering. Employing the Apache Spark framework, the scheme is designed for parallel execution, making it adaptable to real-world applications. For evaluation, we selected 500 documents from the WikiHow and Opinosis datasets, categorized them into five classes, and applied the recall-oriented understudying gisting evaluation (ROUGE) parameters for comparison with measures ROUGE-1, 2, and L. The results include recall scores of 0.3942, 0.0952, and 0.3436 for ROUGE-1, 2, and L, respectively (when using the clustered approach). Through a juxtaposition with existing models such as BERTEXT (with 3-gram, 4-gram) and MATCHSUM, our scheme has demonstrated notable improvements, substantiating its applicability and effectiveness in real-world scenarios.

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Requester mobility for mobile crowdsensing system: A dynamic alliance-based incentive mechanism

Xu,

Sun,

Sun

et al. 2025

Ad Hoc Networks

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Online Context-Aware Task Assignment in Mobile Crowdsourcing via Adaptive Discretization

Cited by 5 publications

References 38 publications

GETS : Sentence Scoring Scheme in Graph-based Extractive Text Summarization for Text Mining Applications

GETS : Sentence Scoring Scheme in Graph-based Extractive Text Summarization for Text Mining Applications

Graph-Based Extractive Text Summarization Sentence Scoring Scheme for Big Data Applications

Requester mobility for mobile crowdsensing system: A dynamic alliance-based incentive mechanism

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