An online multi-source summarization algorithm for text readability in topic-based search

Curiel, Arturo; Gutiérrez-Soto, Claudio; Rojano-Cáceres, José Rafael

doi:10.1016/j.csl.2020.101143

Cited by 9 publications

(2 citation statements)

References 47 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The experiments were conducted on SUMMAC and WIPO corpus. Some other researchers also worked on this area (Curiel et al, 2020). In our proposed research, the method has been introduced for generic single‐document summarization.…”

Section: Related Workmentioning

confidence: 99%

A fusion of variants of sentence scoring methods and collaborative word rankings for document summarization

Verma

Pal

2022

Expert Systems

View full text Add to dashboard Cite

Document summarization is an important task in natural language processing that helps deal with the problem of information overload occurring due to the existence of redundant content. Summary generation with highly relevant contents and maximum coverage is particularly challenging which can only be achieved when redundancy is minimized. This article introduces a novel approach for automatic text summarization based on sentence scoring and collaborative ranking to produce summaries with minimal redundancy and improved overall performance of summarization. The proposed model is a fusion of weighted and unweighted features‐based sentence scoring methods. To learn optimal weights of text features, it has been modelled as an optimization problem. Moreover, the proposed model exploits the strength of collaborative ranking to generate the summary of a given document. Three similarity factors (proximity, significance and singularity)‐based models have been employed to find the similarity between weighted and unweighted sentence scores. The results of the comparison experiment demonstrate that the proposed (PS + Jac) method generates a closer summary to the reference summary with minimal redundant contents. On average, the proposed (PS + Jac) method generates the summaries with 61% accurate contents with greater improved rates up to 40%. The statistical testing also confirms that the performance improvement is significant at a 5% level of significance.

show abstract

Section: Related Workmentioning

confidence: 99%

A fusion of variants of sentence scoring methods and collaborative word rankings for document summarization

Verma

Pal

2022

Expert Systems

View full text Add to dashboard Cite

show abstract

“…Book readability assessment, also known as the book reading di culty level, is a natural language processing (NLP) task that involves classifying reading learning resources based on the book reading complexity [4]. ere has been a systematic and technical technique for comprehending the objective and subjective aspects associated with book readability, better helping readers in understanding more di cult books, or properly determining the job of book reading di culty since the nineteenth century [5]. Book readability has been characterized as the totality of all variables that impact readers' knowledge of TCFL materials, reading speed, and interest in book content, based on the research of these systems.…”

Section: Introductionmentioning

confidence: 99%

Readability Evaluation of Books in Chinese as a Foreign Language Using the Machine Learning Algorithm

Ji¹

2022

Mobile Information Systems

View full text Add to dashboard Cite

The arrival of the boom of teaching Chinese as a foreign language (TCFL) and personalized learning has led to a sharp increase in the demand for the Chinese language reading material. There are numerous reading materials available in Chinese for foreign students and learners to read and evaluate. The high-quality TCFL reading materials with reasonable arrangement can provide convenience for learners with different reading comprehension, interpretation abilities, and levels to master a language more quickly. Therefore, this study carries out an automatic readability assessment of books in Chinese as a foreign language. This paper comprehensively considers the factors affecting the difficulty of reading materials from the perspective of Chinese ontology based on the existing readability assessment research. Using natural language processing and a database management system to extract the features of books in Chinese as a foreign language, the text readability is evaluated with a statistical machine learning algorithm. The model is optimized by feature selection and sorting feature selection technology. The packaging feature selection technology is introduced to optimize the algorithm performance. The feature sets and each independent feature in the three dimensions of word meaning, part of speech, and discourse were optimized by the machine learning regression model based on certain evaluation indexes. Moreover, this work examined that the regression model is effective at identifying and recommending simpler textbooks for learning with difficult foreign language materials. For high-proficient learners, this approach significantly improves performance and measurement efficiency of reading books.

show abstract

Abstractive text summarization employing ontology-based knowledge-aware multi-focus conditional generative adversarial network (OKAM-CGAN) with hybrid pre-processing methodology

Sriramya

2022

Multimed Tools Appl

View full text Add to dashboard Cite

An online multi-source summarization algorithm for text readability in topic-based search

Cited by 9 publications

References 47 publications

A fusion of variants of sentence scoring methods and collaborative word rankings for document summarization

A fusion of variants of sentence scoring methods and collaborative word rankings for document summarization

Readability Evaluation of Books in Chinese as a Foreign Language Using the Machine Learning Algorithm

Abstractive text summarization employing ontology-based knowledge-aware multi-focus conditional generative adversarial network (OKAM-CGAN) with hybrid pre-processing methodology

Contact Info

Product

Resources

About