A novel approach for text summarization using optimal combination of sentence scoring methods

Verma, Pradeepika; Om, Hari

doi:10.1007/s12046-019-1082-4

Cited by 21 publications

(10 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…A sentence may contain various features of the text on the basis of which a score can be assigned to the sentence. In the proposed approach, eight text features (Verma & Om, 2019a) have been considered as follows: Significant words: Significance of a word conveys its contribution of important information in a sentence. A sentence with significant words is also considered significant.…”

Section: Proposed Methodsmentioning

confidence: 99%

“…Ferreira et al (Ferreira et al, 2013) worked on this problem and found the best combinations of sentence scoring techniques using quantitative and qualitative assessments. Similarly, Verma and Om (Verma & Om, 2019a) also proposed a metaheuristic-based approach to find several good combinations of the sentence scoring techniques and test these combinations on DUC datasets.…”

Section: Related Workmentioning

confidence: 99%

Section: Feature Extractionmentioning

confidence: 99%

See 2 more Smart Citations

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

Verma

Pal

2022

Expert Systems

Self Cite

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: Proposed Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

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

Verma

Pal

2022

Expert Systems

Self Cite

View full text Add to dashboard Cite

show abstract

“…MirShojaee et al [18] propose a biogeography-based metaheuristic optimization method (BBO) for extractive text summarization. In [19], authors try to generate optimal combinations of sentence scoring methods and their respective optimal weights for extracting the sentences with the help of a metaheuristic approach known as teachinglearning-based optimization.…”

Section: Related Workmentioning

confidence: 99%

User-Oriented Summaries Using a PSO Based Scoring Optimization Method

Monte

Lanzarini

Bariviera

et al. 2019

Entropy

View full text Add to dashboard Cite

Automatic text summarization tools have a great impact on many fields, such as medicine, law, and scientific research in general. As information overload increases, automatic summaries allow handling the growing volume of documents, usually by assigning weights to the extracted phrases based on their significance in the expected summary. Obtaining the main contents of any given document in less time than it would take to do that manually is still an issue of interest. In this article, a new method is presented that allows automatically generating extractive summaries from documents by adequately weighting sentence scoring features using Particle Swarm Optimization. The key feature of the proposed method is the identification of those features that are closest to the criterion used by the individual when summarizing. The proposed method combines a binary representation and a continuous one, using an original variation of the technique developed by the authors of this paper. Our paper shows that using user labeled information in the training set helps to find better metrics and weights. The empirical results yield an improved accuracy compared to previous methods used in this field.

show abstract

“…They compute the importance of a sentence in extractive single and multi-document summarizations. The work of Verma and Om (2019) extracts the summary sentences with the help of a meta heuristic approach known as teaching-earning-based optimization. The work of Sariki et al (2019) contains three different concurrent pipelines to improve the effectiveness of the summarization process.…”

Section: Introductionmentioning

confidence: 99%

Automatic Text Summarization by Providing Coverage, Non-Redundancy, and Novelty Using Sentence Graph

Krishnaveni

Balasundaram

2022

Journal of Information Technology Research

View full text Add to dashboard Cite

The day-to-day growth of online information necessitates intensive research in automatic text summarization (ATS). The ATS software produces summary text by extracting important information from the original text. With the help of summaries, users can easily read and understand the documents of interest. Most of the approaches for ATS used only local properties of text. Moreover, the numerous properties make the sentence selection difficult and complicated. So this article uses a graph based summarization to utilize structural and global properties of text. It introduces maximal clique based sentence selection (MCBSS) algorithm to select important and non-redundant sentences that cover all concepts of the input text for summary. The MCBSS algorithm finds novel information using maximal cliques (MCs). The experimental results of recall oriented understudy for gisting evaluation (ROUGE) on Timeline dataset show that the proposed work outperforms the existing graph algorithms Bushy Path (BP), Aggregate Similarity (AS), and TextRank (TR).

show abstract

A novel approach for text summarization using optimal combination of sentence scoring methods

Cited by 21 publications

References 40 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

User-Oriented Summaries Using a PSO Based Scoring Optimization Method

Automatic Text Summarization by Providing Coverage, Non-Redundancy, and Novelty Using Sentence Graph

Contact Info

Product

Resources

About