A Comprehensive Survey on Topic Modeling in Text Summarization

Mohan, G. Bharathi; Kumar, R. Prasanna

doi:10.1007/978-981-16-8721-1_22

Cited by 43 publications

(2 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…SummVis [22], an open-source tool, allows us to visualize, generate a summary, and analyze the summarization models and the evaluation metrics used. Topic modeling has been recently used in text summarization to identify hidden topics in the document [23]. Latent Dirichlet allocation (LDA) performs better than latent semantic analysis (LSA) if the number of features increases in the sentences.…”

Section: Literature Reviewmentioning

confidence: 99%

A hybrid approach for text summarization using semantic latent Dirichlet allocation and sentence concept mapping with transformer

Gurusamy,

Rengarajan,

Srinivasan

2023

IJECE

View full text Add to dashboard Cite

<span lang="EN-US">Automatic text summarization generates a summary that contains sentences reflecting the essential and relevant information of the original documents. Extractive summarization requires semantic understanding, while abstractive summarization requires a better intermediate text representation. This paper proposes a hybrid approach for generating text summaries that combine extractive and abstractive methods. To improve the semantic understanding of the model, we propose two novel extractive methods: semantic latent Dirichlet allocation (semantic LDA) and sentence concept mapping. We then generate an intermediate summary by applying our proposed sentence ranking algorithm over the sentence concept mapping. This intermediate summary is input to a transformer-based abstractive model fine-tuned with a multi-head attention mechanism. Our experimental results demonstrate that the proposed hybrid model generates coherent summaries using the intermediate extractive summary covering semantics. As we increase the concepts and number of words in the summary the rouge scores are improved for precision and F1 scores in our proposed model.</span>

show abstract

Section: Literature Reviewmentioning

confidence: 99%

A hybrid approach for text summarization using semantic latent Dirichlet allocation and sentence concept mapping with transformer

Gurusamy,

Rengarajan,

Srinivasan

2023

IJECE

View full text Add to dashboard Cite

show abstract

“…In essence, abstraction refers to the human ability to focus on simpler explanations of observation and conceptualisation, alongside reasoning (Goldstone & Barsalou, 1998). In the context of utilising MLTs for data synthesis of SLR data, abstraction involves the identification of key themes or concepts that are relevant to the deductive analysis and representing them in a more concise and organised way (Mohan & Kumar, 2022). As such, it allows for complex data sets, such as those extracted during a SLR, to be simplified to facilitate integration and analysis.…”

Section: Introductionmentioning

confidence: 99%

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

Atkinson

2023

Social Science Computer Review

View full text Add to dashboard Cite

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.

show abstract

Text Summarization for Big Data Analytics: A Comprehensive Review of GPT 2 and BERT Approaches

Bharathi Mohan,

Prasanna Kumar,

Parathasarathy

et al. 2023

Internet of Things

View full text Add to dashboard Cite

A Comprehensive Survey on Topic Modeling in Text Summarization

Cited by 43 publications

References 12 publications

A hybrid approach for text summarization using semantic latent Dirichlet allocation and sentence concept mapping with transformer

A hybrid approach for text summarization using semantic latent Dirichlet allocation and sentence concept mapping with transformer

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

Text Summarization for Big Data Analytics: A Comprehensive Review of GPT 2 and BERT Approaches

Contact Info

Product

Resources

About