Linguistic summarization to support supply network decisions

Aydoğan, Sena; Kremer, Gül E. Okudan; Akay, Diyar

doi:10.1007/s10845-020-01677-9

Cited by 5 publications

(2 citation statements)

References 48 publications

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“…The combination of the followings gives the total number of sentences: the number of quantifiers, the number of summarizers, and (if it exists) the number of pre-summarizers. The most insightful and practical sentence reveals the highest degree of truth [ 63 ].…”

Section: Linguistic Summarization Techniquesmentioning

confidence: 99%

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Linguistic summarization of visual attention and developmental functioning of young children with autism spectrum disorder

Öztürk,

Aydoğan,

Kök

et al. 2024

Health Inf Sci Syst

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Diagnosing autism spectrum disorder (ASD) in children poses significant challenges due to its complex nature and impact on social communication development. While numerous data analytics techniques have been proposed for ASD evaluation, the process remains time-consuming and lacks clarity. Eye tracking (ET) data has emerged as a valuable resource for ASD risk assessment, yet existing literature predominantly focuses on predictive methods rather than descriptive techniques that offer human-friendly insights. Interpretation of ET data and Bayley scales, a widely used assessment tool, is challenging for ASD assessment of children. It should be understood clearly to perform better analytic tasks on ASD screening. Therefore, this study addresses this gap by employing linguistic summarization techniques to generate easily understandable summaries from raw ET data and Bayley scales. By integrating ET data and Bayley scores, the study aims to improve the identification of children with ASD from typically developing children (TD). Notably, this research represents one of the pioneering efforts to linguistically summarize ET data alongside Bayley scales, presenting comparative results between children with ASD and TD. Through linguistic summarization, this study facilitates the creation of simple, natural language statements, offering a first and unique approach to enhance ASD screening and contribute to our understanding of neurodevelopmental disorders.

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Section: Linguistic Summarization Techniquesmentioning

confidence: 99%

“…If there is more than one summarizer in the quantified sentence created, their intersection is obtained with the t-norm operator and included in the truth degree calculation [ 63 ]. Let's and are fuzzy sets defined in universal set, and the membership functions of these sets are ve .…”

Section: Linguistic Summarization Techniquesmentioning

confidence: 99%