Deep Extractive Text Summarization

Bhargava, Rupal; Sharma, Yashvardhan

doi:10.1016/j.procs.2020.03.191

Cited by 24 publications

(7 citation statements)

References 14 publications

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“…The model was then evaluated using ROUGE on the DUC and CNN datasets. The proposed model shows better performance empirically than other comparison methods [20].…”

Section: Research Conducted Bymentioning

confidence: 86%

An Extractive Text Summarization based on Candidate Summary Sentences using Fuzzy-Decision Tree

Widyassari¹,

Noersasongko²,

Syukur³

et al. 2022

IJACSA

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This study aims to predict candidate summary sentences in extractive summary using the Fuzzy-Decision Tree method. The fuzzy method is quite superior and the most widely used in extractive summaries, because Fuzzy has advantages in calculations that are not cryptic, so it is able to calculate uncertain possibilities. However, in its implementation, the fuzzy rule generation process is often carried out randomly or based on expert understanding so that it does not represent the distribution of the data. Therefore, in this study, a Decision Tree (DT) technique was added to generate fuzzy rules. From the fuzzy final result, important sentences are obtained that are candidates for summary sentences. The performance of our proposed method was tested on the 2002 DUC dataset in the ROUGE-1 evaluation. The results showed that our method outperformed other methods (baseline and sentence ranking) with an average precision of 0.882498, Recall 0.820443 and F Measure 0.882498 with CI for F1 0.821-0.879 at the 95% confidence level.

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“…The model was then evaluated using ROUGE on the DUC and CNN datasets. The proposed model shows better performance empirically than other comparison methods [20].…”

Section: Research Conducted Bymentioning

confidence: 86%

An Extractive Text Summarization based on Candidate Summary Sentences using Fuzzy-Decision Tree

Widyassari¹,

Noersasongko²,

Syukur³

et al. 2022

IJACSA

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“…We suggest here the term 'adaptation' with a broader meaning because it includes pragmatic and semiotic transformations as well as all other types of text transformations. The term 'simplification' reflects semantic, structural (Collados, 2013), and compositional transformations (Bhargava & Sharma, 2020) on the level of a sentence, complex syntactical unit, and the whole text (Fig. 4).…”

Section: • Addressor (Professional)mentioning

confidence: 99%

“…Text compression is seen as abstractive text summarization which is performed on semantic level generating sentences reflecting meanings of bigger parts of the summarized text. Text reduction is extractive text summarization done through choosing salient sentences (Bhargava & Sharma, 2020;Cao & Zhuge, 2020).…”

Section: • Addressor (Professional)mentioning

confidence: 99%

Approaches to Text Simplification: Can Computer Technologies Outdo a Human Mind?

Pervukhina

Basenko

Ryabtseva

et al. 2021

gema

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Narrowly specialized information is addressed to a limited circle of professionals though it provokes interest among people without specialized education. This gives rise to a need for the popularization of scientific information. This process is carried out through simplified texts as a kind of secondary texts that are directly aimed at the addressee. Age, language proficiency and background knowledge are the main features which are usually taken into consideration by the author of the secondary text who makes changes in the text composition, as well as in its pragmatics, semantics and syntax. This article analyses traditional approaches to text simplification, computer simplification and summarization. The authors compare human-authored simplification of literary texts with the newest trends in computer simplification to promote further development of machine simplification tools. It has been found that the samples of simplified scientific texts seem to be more natural than the samples of simplified literary texts since technical background knowledge can be processed with machine tools. The authors have come to the conclusion that literary and technical texts should imply different approaches for adaptation and simplification. In addition, personal readers’ experience plays a great part in finding the implications in literary texts. In this respect it might be reasonable to create separate engines for simplifying and adapting texts from diverse spheres of knowledge. Keywords Text Simplification; Natural Language Processing (NLP); Pragmatic Adaptation; Professional Communication; Literary Texts

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“…Reading an entire article has become a challenge for everyone to get the information they need (Afsharizadeh et al, 2018;Bhargava and Sharma, 2020). Not to mention that if the information sought is quite difficult and/or not found in one source, of course, more reading material and time will be needed (Sinha et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Android application for extractive text summarization

Hardiyanto¹,

Fitrianah²

2021

LHTN

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Purpose The purpose of this study is to provide an automatic text summarization experience using the extractive method within an application implemented on Android. Design/methodology/approach Scrum framework is used to keep pace with trends and enhance the development process of this application. The code implementation using Flutter Software Development Kit in client-side and Python with Flask on server-side is aimed to have better integration and maintenance process. The authors had done black-box testing and white-box testing to ensure the application runs as expected for both the behavior and code. Findings This application will provide full functionality to perform the summarization process, starting from the input to the presentation of the results of text summarization. Originality/value Besides summarizing text, the application is also capable of copying and saving the result into the device.

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Deep Extractive Text Summarization

Cited by 24 publications

References 14 publications

An Extractive Text Summarization based on Candidate Summary Sentences using Fuzzy-Decision Tree

An Extractive Text Summarization based on Candidate Summary Sentences using Fuzzy-Decision Tree

Approaches to Text Simplification: Can Computer Technologies Outdo a Human Mind?

Android application for extractive text summarization

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