Text Summarization within the Latent Semantic Analysis Framework: Comparative Study

Badry, Rasha M.; Eldin, Ahmed Sharaf; Elzanfally, Doaa Saad

doi:10.5120/14060-2366

Cited by 12 publications

(2 citation statements)

References 8 publications

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“…The cells that result contain information about the reoccurrence of a word in a paragraph [54]. After applying the SVD, and based on information obtained from it, an additional step is carried out, "sentence selection", in which the "most characteristic parts of text" are selected to illustrate ideas that are essential in the body of text [55].…”

Section: Natural Language Processing Approachesmentioning

confidence: 99%

Hit or Miss? Evaluating the Potential of a Research Niche: A Case Study in the Field of Virtual Quality Management

et al. 2019

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When knowledge is developed fast, as it is the case so often nowadays, one of the main difficulties in initiating new research in any field is to identify the domain's specific state-of-the-art and trends. In this context, to evaluate the potential of a research niche by assisting the literature review process and to add a new and modern large-scale and automated dimension to it, the paper proposes a methodology that uses "Latent Semantic Analysis" (LSA) for identifying trends, focused within the knowledge space created at the intersection of three sustainability-related methodologies/concepts: "virtual Quality Management" (vQM), "Industry 4.0", and "Product Life-Cycle" (PLC). The LSA was applied to a significant number of scientific papers published around these concepts to generate ontology charts that describe the knowledge structure of each by the frequency, position, and causal relation of associated notions. These notions are combined for defining the common high-density knowledge zone from where new technological solutions are expected to emerge throughout the PLC. The authors propose the concept of the knowledge space, which is characterized through specific descriptors with their own evaluation scales, obtained by processing the emerging information as identified by a combination of classic and innovative techniques. The results are validated through an investigation that surveys a relevant number of general managers, specialists, and consultants in the field of quality in the automotive sector from Romania. This practical demonstration follows each step of the theoretical approach and yields results that prove the capability of the method to contribute to the understanding and elucidation of the scientific area to which it is applied. Once validated, the method could be transferred to fields with similar characteristics. Even if their creators endowed them with a clear meaning at an incipient stage, when they become more popular in an emerging area, these concepts are quickly surrounded by a large amount of new knowledge that is developed with an amazing speed, enriching and enlarging their initial sphere.The "virtual Quality Management" (vQM) concept could be a significant example for the circumstances described previously. It is born through a semantic operation, joining two established and mature concepts: "virtual" and "QM", thus it is representative for an area which is in a period of high dynamic development and of interest for companies preoccupied with sustainability from the perspective of operations management and organizational culture.In this context in which the amount of information relating to new concepts quickly reaches unmanageable levels, regardless of the field, solutions that can analyze extended documentation with the purpose of disambiguating information and capturing the essentials, thus creating knowledge, become the focus of attention and gain in importance. Traditional solutions for that purpose lay in the literature review process, trying to collect, select, filter, and struc...

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Section: Natural Language Processing Approachesmentioning

confidence: 99%

Hit or Miss? Evaluating the Potential of a Research Niche: A Case Study in the Field of Virtual Quality Management

et al. 2019

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“…In this paper, two different approaches are proposed for performing extractive text summarization on Marathi text documents. The first approach focuses upon using latent semantic analysis (LSA) that deploys a semantic identification and correlation of sentences between a document [8]. This is achieved using the singular value decomposition (SVD) technique.…”

Section: Introductionmentioning

confidence: 99%

Marathi Extractive Text Summarization using Latent Semantic Analysis and Fuzzy Algorithms

Math¹

2023

CIML

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Extractive text summarization involves the retention of only the most important sentences in a document. In the past, multiple approaches involving both statistical and machine learning-based methods have been used for this task. The crucial step in extractive text summarization is getting the right ranking order of sentences in the document in terms of their importance. Singular value decomposition or SVD algorithm based on latent semantic analysis focuses on recognizing the sections in the document which are related in terms of their semantic nature. Fuzzy algorithms involve reasoning of the priority order of the sentences using fuzzy logic unlike the use of discrete values. While significant work has been done for extractive text summarization in English and other foreign languages, there is ample scope for improving the performance of systems when dealing with Marathi text. In this paper, SVD and fuzzy algorithms are proposed for performing extractive text summarization on Marathi documents. Work is done upon the modeling principle, data flow, and parameters of these algorithms such that they are best suited for the task. An analysis of the characteristics of both these techniques is conducted to compare their benefits and shortcomings. The performance of both the algorithms is evaluated on a document dataset using standard performance metrics including the ROUGE metric. An unbiased comparison of both these techniques is carried out to inform the applicability of them, especially when working with Marathi or in general, non-English text.

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A Semantic Text Summarization Model for Arabic Topic-Oriented

Badry

Moawad

2019

Advances in Intelligent Systems and Computing

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Text Summarization within the Latent Semantic Analysis Framework: Comparative Study

Cited by 12 publications

References 8 publications

Hit or Miss? Evaluating the Potential of a Research Niche: A Case Study in the Field of Virtual Quality Management

Hit or Miss? Evaluating the Potential of a Research Niche: A Case Study in the Field of Virtual Quality Management

Marathi Extractive Text Summarization using Latent Semantic Analysis and Fuzzy Algorithms

A Semantic Text Summarization Model for Arabic Topic-Oriented

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