A Review: Abstractive Text Summarization Techniques using NLP

Batra, Pooja; Chaudhary, Sarika; Bhatt, Kavya; Varshney, Saloni; Verma, Srashti

doi:10.1109/icaccm50413.2020.9213079

Cited by 15 publications

(8 citation statements)

References 9 publications

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“…Authors have done three level of analysis namely Document Level analysis, Sentence Level analysis, and Entity and Aspect Level analysis. Fundamental concepts and approaches to automatic text summarization is presented in [5]. Authors have proposed various methods of abstractive text summarization like a recurrent neural network, long short-term memory network, encoder-decoder model, and pointer generator mechanism.…”

Section: Related Workmentioning

confidence: 99%

Transfer Learning based Automated Essay Summarization

Rohith

Srinivas

Deepika

et al. 2023

IJRITCC

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The human evaluation of essays has become a very time-consuming process as the number of schools and universities has grown. The available software entities are unable to assess the sentiment associated with essays. Thus, we propose a model using Natural Language Processing to assess the essay based on both grammar and sentiment associated with the essay by using linear regression and ULMFiT (Universal Language Model Fine-tuning for Text Classification) models. Evaluation of essay is done in two parts. Part one is on essay grading with respect to grammar with maximum 12 and minimum 0 grade points and in part two score of 0/1 for sentiment analysis with 0 being negative and 1 being positive. The model can be used to score the essay and discard any essay with a score less than a specified value or specified sentiment score.

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Section: Related Workmentioning

confidence: 99%

Transfer Learning based Automated Essay Summarization

Rohith

Srinivas

Deepika

et al. 2023

IJRITCC

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“…Text summarization method can be utilized for different purposes such as in email summary, reviews from claiming movies, news abstraction, framework about learner notes, rundown data to specialist. Furthermore legislature officials, businessman, summarize the therapeutic information to doctors and summarize the authoritative archive [16].…”

Section: Text Summarizationmentioning

confidence: 99%

“…Abstractive script outline has a great compression rate besides lessens duplicated in this way produces an additional important and exact outline with decrease vagueness. Its employments machine learning strategies and far-reaching NLP [7], [16].…”

Section: B Abstractive Text Summarizationmentioning

confidence: 99%

Convolutional Recurrent Neural Networks for Text Lecture Summarization

2022

IJCCCE

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Text summarization can be utilized for variety type of purposes; one of them for summary lecture file. A long document expended long time and large capacity. Since it may contain duplicated information, more over, irrelevant details that take long period to access relevant information. Summarization is a technique which provides the primary points of the whole document, and in the same time it will indicates the majority of the information in a small amount of time. For this reason it can save user time, decrease storage, and increase transfer speed to transmit through the internet. The summarization process will eliminate duplicated data, unimportant information, and also replace complex expression with simpler expression. The proposed method is using convolutional recurrent neural network deep model as a method for abstractive text summarization of lecture file that will be great helpful to students to address lecture notes. This method proposes a novel encoder-decoder deep model including two deep model networks which are convolutional and recurrent. The encoder part which consists of two convolutional layers followed by three recurrent layers of type bidirectional long short term memory. The decoder part which consists of one recurrent layer of type long short term memory. And also using attention mechanism layer. The proposed method training using standard CNN/Daily Mail dataset that achieved 92.90% accuracy. Index Terms— Text Summarization, Deep Learning, Convolutional Neural Network, Long Short Term Memory, Convolutional Recurrent Neural Network.

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“…As such, it allows for complex data sets, such as those extracted during a SLR, to be simplified to facilitate integration and analysis. This is achieved through filtering crucial elements while excluding unrelated or less significant details (Batra et al, 2020; Kallimani, 2018). Therefore, if a research project, such as this one, utilises data extraction based on preconceived templates, when combined with DQA, abstraction allows researchers to leverage the strengths of qualitative analysis methods with MLTs to synthesise and obtain meaningful insights from qualitative data in a more efficient and scalable manner.…”

Section: Introductionmentioning

confidence: 99%

Cheap, Quick, and Rigorous: Artificial Intelligence and the Systematic Literature Review

Atkinson

2023

Social Science Computer Review

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The systematic literature review (SLR) is the gold standard in providing research a firm evidence foundation to support decision-making. Researchers seeking to increase the rigour, transparency, and replicability of their SLRs are provided a range of guidelines towards these ends. Artificial Intelligence (AI) and Machine Learning Techniques (MLTs) developed with computer programming languages can provide methods to increase the speed, rigour, transparency, and repeatability of SLRs. Aimed towards researchers with coding experience, and who want to utilise AI and MLTs to synthesise and abstract data obtained through a SLR, this article sets out how computer languages can be used to facilitate unsupervised machine learning for synthesising and abstracting data sets extracted during a SLR. Utilising an already known qualitative method, Deductive Qualitative Analysis, this article illustrates the supportive role that AI and MLTs can play in the coding and categorisation of extracted SLR data, and in synthesising SLR data. Using a data set extracted during a SLR as a proof of concept, this article will include the coding used to create a well-established MLT, Topic Modelling using Latent Dirichlet allocation. This technique provides a working example of how researchers can use AI and MLTs to automate the data synthesis and abstraction stage of their SLR, and aide in increasing the speed, frugality, and rigour of research projects.

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A Review: Abstractive Text Summarization Techniques using NLP

Cited by 15 publications

References 9 publications

Transfer Learning based Automated Essay Summarization

Transfer Learning based Automated Essay Summarization

Convolutional Recurrent Neural Networks for Text Lecture Summarization

Cheap, Quick, and Rigorous: Artificial Intelligence and the Systematic Literature Review

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