Multi-document extractive text summarization based on firefly algorithm

Tomer, Minakshi; Kumar, Manoj

doi:10.1016/j.jksuci.2021.04.004

Cited by 24 publications

(17 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A Firefly-based text summarizing (FbTS) [17], which uses the firefly algorithm to perform text summarization, was proposed by Tomer& Kumar. According to the researchers, multi-document extraction of automated text summarizing using the ROUGE score improves Firefly algorithm (FA) performance.…”

Section: B Other Approaches In Text Summarizationmentioning

confidence: 99%

OCR-based Hybrid Image Text Summarizer using Luhn Algorithm with FinetuneTransformer Modelsfor Long Document

Singco¹,

Trillo²,

Abalorio³

et al. 2023

IJETAE

View full text Add to dashboard Cite

The accessibility of an enormous number of image text documents on the internet has expanded the opportunities to develop a system for image text recognition with text summarization. Several approaches used in ATS in the literature are based on extractive and abstractive techniques; however, few implementations of the hybrid approach were observed. This paper employed state-of-the-art transformer models with the Luhn algorithm for extracted texts using Tesseract OCR. Nine models were generated and tested using the hybrid text summarization approach. Using ROUGE metrics, we compared the proposed system finetune abstractive models against existing abstractive models that use the same dataset Xsum. As a result, the finetune model got the highest ROUGE score during evaluation; in ROUGE-1 score was 57%, the ROUGE-2 score was 43%, and the ROUGE-L score was 42%. Furthermore, even when better algorithms and models were available for summarization, the Luhn algorithm and T5 finetune model provided significant results.

show abstract

Section: B Other Approaches In Text Summarizationmentioning

confidence: 99%

OCR-based Hybrid Image Text Summarizer using Luhn Algorithm with FinetuneTransformer Modelsfor Long Document

Singco¹,

Trillo²,

Abalorio³

et al. 2023

IJETAE

View full text Add to dashboard Cite

show abstract

“…Word tokenization arrangement of chara phrases) that are called characters such as pun in a list that is further [45].…”

Section: Word Tokenizatiomentioning

confidence: 99%

“…Word tokenization is the division of an arrangement of characters to parts (words and phrases) that are called tokens, and reject certain characters such as punctuation. Tokens are stored in a list that is further processed Webster and Kit [45].…”

Section: Word Tokenizationmentioning

confidence: 99%

Text Document Clustering Approach by Improved Sine Cosine Algorithm

Radomirović,

Jovanović,

Nikolić

et al. 2023

ITC

View full text Add to dashboard Cite

Due to the vast amounts of textual data available in various forms such as online content, social media comments, corporate data, public e-services and media data, text clustering has been experiencing rapid development. Text clustering involves categorizing and grouping similar content. It is a process of identifying significant patterns from unstructured textual data. Algorithms are being developed globally to extract useful and relevant information from large amounts of text data. Measuring the significance of content in documents to partition the collection of text data is one of the most important obstacles in text clustering. This study suggests utilizing an improved metaheuristics algorithm to fine-tune the K-means approach for text clustering task. The suggested technique is evaluated using the first 30 unconstrained test functions from the CEC2017 test-suite and six standard criterion text datasets. The simulation results and comparison with existing techniques demonstrate the robustness and supremacy of the suggested method.

show abstract

“…They solved these problems using the shark smell optimization approach with the normalized Google Distance and Word Mover's Distance functions and found a good performance in the generated summaries. Tomar and Kumar (Tomer & Kumar, 2021) proposed a multi‐document extractive summarization system to address the problem of relevancy and readability in the generated summaries. They had taken into account three factors: topic relation, cohesion and readability, and used the firefly optimization algorithm to address these problems.…”

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

Multi-document extractive text summarization based on firefly algorithm

Cited by 24 publications

References 25 publications

OCR-based Hybrid Image Text Summarizer using Luhn Algorithm with FinetuneTransformer Modelsfor Long Document

OCR-based Hybrid Image Text Summarizer using Luhn Algorithm with FinetuneTransformer Modelsfor Long Document

Text Document Clustering Approach by Improved Sine Cosine Algorithm

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

Contact Info

Product

Resources

About