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<p>Unsupervised Extractive News Articles Summarization leveraging Statistical, Topic-Modelling and Graph-based Approaches</p>

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Title Statement <p>Unsupervised Extractive News Articles Summarization leveraging Statistical, Topic-Modelling and Graph-based Approaches</p>
 
Added Entry - Uncontrolled Name Barman, Utpal ; The Assam Kaziranga University, Jorhat 785 006, Assam
Barman, Vishal ; GIMT, Guwahati, Assam.
Choudhury, Nawaz Khan; Girijananda Chowdhury Institute of Management and Technology, Guwahati 781017, Assam
Rahman, Mustafizur ; Girijananda Chowdhury Institute of Management and Technology, Guwahati 781017, Assam
Sarma, Shikhar Kumar; Gauhati University, Guwahati 781014, Assam
 
Uncontrolled Index Term LSA, NLP, ROUGE, TextRank, TF-IDF
 
Summary, etc. Due to the presence of large amounts of data and its exponential level generation, the manual approach of summarization takes more time, is biased, and needs linguistic professional experts. To avoid these substantial issues or to generate a succinct summary report, automatic text summarization is very much important. Three different approaches namely the statistical approach such as Term Frequency Inverse Document Frequency(TF-IDF), the topic modeling approach such as Latent Semantic Analysis (LSA), and graph-based approaches such as TextRank were applied to generate a concise summary for the benchmark the British Broadcasting Corporation (BBC) news articles summarization dataset. The domain-specific implementations of each approach in the five domains of the dataset and domain-agnostic prospects were explored in the paper while drawing various insights. The generated summaries were evaluated using the Recall-Oriented Understudy for Gisting Evaluation (ROUGE) framework, leveraging precision, recall, and f-measure metrics. The approaches were not only able to achieve a commendable ROUGE score but also outperform the previous works on the dataset
 
Publication, Distribution, Etc. Journal of Scientific & Industrial Research
2022-09-07 19:53:04
 
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http://op.niscair.res.in/index.php/JSIR/article/view/53185
 
Data Source Entry Journal of Scientific & Industrial Research; ##issue.vol## 81, ##issue.no## 09 (2022): Journal of Scientific & Industrial Research
 
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Nonspecific Relationship Entry http://op.niscair.res.in/index.php/JSIR/article/download/53185/465572567
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