Improving readability through extractive summarization for learners with reading difficulties

Nandhini, K.; Balasundaram, S. R.

doi:10.1016/j.eij.2013.09.001

Cited by 38 publications

(23 citation statements)

References 24 publications

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“…The link between one nodes to another depicts the continuity of the meaning of the documents. The strongest and weakest relationship denotes the relevancy of the sentence flow [16]. Most of the graph-based text summarization have been carried out using the LexRank and TexRank method.…”

Section: Related Workmentioning

confidence: 99%

Multi-Documents Extractive Text Summarization using Node Centrality

Kuriakose,

Umadevi

2020

IJEAT

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The advancement of technologies produce vast amount of data over the internet. The massive amount of information flooded in the webpages become more difficult to extract the meaningful insights. Social media websites are playing major role in publishing news events on the similar topic with different contents. Extracting the hidden information from the multiple webpages are tedious job for researchers and industrialists. This paper mainly focuses on gathering information from multiple webpages and to produce summary from those contents under similar topic. Multi-document extractive summarization has been developed using the graph based text summarization method. Proposed method builds a graph between the multi-documents using the Katz centrality of nodes. The performance of proposed GeSUM (Graph based Extractive Summarization) is evaluated with the ROUGE metrics.

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Section: Related Workmentioning

confidence: 99%

Multi-Documents Extractive Text Summarization using Node Centrality

Kuriakose,

Umadevi

2020

IJEAT

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“…Besides sentence reduction and paraphrasing, there are several ways to summarize the text document, such as sentence split and join, generalization concept and specification concept [50]. Basically, automated text summarization has two types which are extractive and abstractive that have been described in The big challenge in text summarization research is how to produce readable summary [3], [4], [51], it means that the gap between summary result and reader understanding is not high. The elaboration of current text summarization can be made based on summarization approach (the way to summarize) [49], [50]; type of summary result [3], [52]- [54]; requirement of user [48], [55]; output style [48]; sentence weight [48], [56]; summarization impact [48]; available dataset [53], [56], [57] document type, summary unit [57], sources [57]; target and task-based [48]; language-based [48], [56] approach, objective of text summarization [48]; indicator representation [48], [52]; and document types [48], [57] as provided in Figure 5.…”

Section: Taxonomy Of Text Summarizationmentioning

confidence: 99%

Sequential Pattern Mining and Deep Learning to Enhance Readability of Indonesian Text Summarization

Maylawati¹

2019

IJATCSE

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Readability is a big challenge that must be solved in automatic text summarization research. The aim of this study is to comprehensively and systematically investigate the literatures that are related with text summarization needs, in particular, to prepare efficient algorithm for Indonesian text using Deep Learning and Sequential Pattern Mining. Evidence from previous literatures shows that there are several studies which use Sequential Pattern Mining to extract representation of text for document clustering and classification of Indonesian text. However, not much attention was given to text representation in document summarization, specifically for Indonesian language. As readability is a major concern in the text summarization community, determining a better text representation to maintain the meaning of the generated text summary is deemed necessary. This paper gives an opportunity to take a deeper look into how to design an efficient and effective text representation which can further enhance text summarization readability. Besides the general systematic literature review, we discuss an idea to combine Deep Learning and Sequential Pattern Mining to improve readability of summary result to develop in the future.

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“…Neural network technique is used for summary extraction of science and social subjects in the educational text. [6] Synonyms based approach is used when the text summary is not target oriented or is very less i.e. less than 5% of the whole document.…”

Section: Related Workmentioning

confidence: 99%

Document Summarization and Evaluation using Knowledge based Super Set Features

Garg¹,

Chhillar²

2015

IJCA

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Document summarization is an important step while clustering the large no. of digital documents data base. Documents are clustered in accordance with their contents using the document text summary. The document summarization involves the knowledge corpus scheme comprising of corpus coverage, sentence coverage and term coverage weight. Further, three new weights are introduced as super sentence coverage weight, super corpus coverage weight and super term coverage weight. Super coverage weight is based on synonyms of the key words. The quality of document summary improves and diversified when synonyms of key words are also given due weightage in the process of text processing. The evaluation for the document summary quality is based on inner content metrics precision, recall, F-measure method.

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Improving readability through extractive summarization for learners with reading difficulties

Cited by 38 publications

References 24 publications

Multi-Documents Extractive Text Summarization using Node Centrality

Multi-Documents Extractive Text Summarization using Node Centrality

Sequential Pattern Mining and Deep Learning to Enhance Readability of Indonesian Text Summarization

Document Summarization and Evaluation using Knowledge based Super Set Features

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