Use of Genetic Algorithm for Cohesive Summary Extraction to Assist Reading Difficulties

Nandhini, K.; Balasundaram, S. R.

doi:10.1155/2013/945623

Cited by 22 publications

(7 citation statements)

References 23 publications

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“…Nandhini and Balasundaram [8] have introduced a separate method called cohesive summary to monitor reading difficulties with the help of GA. The motive with this piece of work is to make the deletion over accurate mix of sentences that could grow thoughtful over the sentences and how they got bonding using GA.…”

Section: Literature Surveymentioning

confidence: 99%

Effective Cataloging over Diverse Algorithms for Automatic Text Summarization and Its Survey

Pradheeba

J²,

Kj³

et al. 2021

Advances in Parallel Computing Technologies and Applications

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In these days, the measure of information and data accessible over the web is gigantic which prompted the making of automatic text summarization. Text Summarization is the route toward obtaining eminent information from a substantial text record. Automatic Text summarization is being a need of great importance and a fascinating theme with regards to NLP. Given the expansion in size and number of archives accessible on the web, an effective automatic text summarizer is significant. The fundamental test is to cause the computer to comprehend the report given with any expansion and create the outline as the primary rationale. The fundamental main impetus behind this work is to diminish the time and exertion spent by the client on perusing the whole document to understand what is the issue here. Subsequently the paper focuses on procedures accessible in delivering the significant rundown by utilizing different abstractive and extractive techniques

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Section: Literature Surveymentioning

confidence: 99%

Effective Cataloging over Diverse Algorithms for Automatic Text Summarization and Its Survey

Pradheeba

J²,

Kj³

et al. 2021

Advances in Parallel Computing Technologies and Applications

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show abstract

“…Besides, we need to determine the fitness function. Many fitness functions have been proposed for the summarization task in the previous studies (Qazvinian et al , 2008; Zhao and Tang, 2010; Nandhini and Balasundaram, 2013). However, in our case, the proposed fitness function needs to examine the selected sentences based on the user’s perspective.…”

Section: Proposed Approachmentioning

confidence: 99%

An interactive query-based approach for summarizing scientific documents

Bayatmakou

Mohebi

Ahmadi

2021

IDD

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Purpose Query-based summarization approaches might not be able to provide summaries compatible with the user’s information need, as they mostly rely on a limited source of information, usually represented as a single query by the user. This issue becomes even more challenging when dealing with scientific documents, as they contain more specific subject-related terms, while the user may not be able to express his/her specific information need in a query with limited terms. This study aims to propose an interactive multi-document text summarization approach that generates an eligible summary that is more compatible with the user’s information need. This approach allows the user to interactively specify the composition of a multi-document summary. Design/methodology/approach This approach exploits the user’s opinion in two stages. The initial query is refined by user-selected keywords/keyphrases and complete sentences extracted from the set of retrieved documents. It is followed by a novel method for sentence expansion using the genetic algorithm, and ranking the final set of sentences using the maximal marginal relevance method. Basically, for implementation, the Web of Science data set in the artificial intelligence (AI) category is considered. Findings The proposed approach receives feedback from the user in terms of favorable keywords and sentences. The feedback eventually improves the summary as the end. To assess the performance of the proposed system, this paper has asked 45 users who were graduate students in the field of AI to fill out a questionnaire. The quality of the final summary has been also evaluated from the user’s perspective and information redundancy. It has been investigated that the proposed approach leads to higher degrees of user satisfaction compared to the ones with no or only one step of the interaction. Originality/value The interactive summarization approach goes beyond the initial user’s query, while it includes the user’s preferred keywords/keyphrases and sentences through a systematic interaction. With respect to these interactions, the system gives the user a more clear idea of the information he/she is looking for and consequently adjusting the final result to the ultimate information need. Such interaction allows the summarization system to achieve a comprehensive understanding of the user’s information needs while expanding context-based knowledge and guiding the user toward his/her information journey.

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“…Nandhini and Balasundaram [56] have proposed a genetic-based text summarization system to assist in reading difficulties. The system considers informative score, readability score, and sentence similarity scores to weight the sentence of the text.…”

Section: Some Work In Text Summarizationmentioning

confidence: 99%

Applications of Mining Arabic Text: A Review

Al-Radaideh¹

2020

Recent Trends in Computational Intelligence

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Since the appearance of text mining, the Arabic language gained some interest in applying several text mining tasks over a text written in the Arabic language. There are several challenges faced by the researchers. These tasks include Arabic text summarization, which is one of the challenging open areas for research in natural language processing (NLP) and text mining fields, Arabic text categorization, and Arabic sentiment analysis. This chapter reviews some of the past and current researches and trends in these areas and some future challenges that need to be tackled. It also presents some case studies for two of the reviewed approaches.

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Use of Genetic Algorithm for Cohesive Summary Extraction to Assist Reading Difficulties

Cited by 22 publications

References 23 publications

Effective Cataloging over Diverse Algorithms for Automatic Text Summarization and Its Survey

Effective Cataloging over Diverse Algorithms for Automatic Text Summarization and Its Survey

An interactive query-based approach for summarizing scientific documents

Applications of Mining Arabic Text: A Review

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