Special issue on International Conference on Computing and Communication Networks

Gupta, Deepak

doi:10.1111/exsy.13167

Cited by 1 publication

(1 citation statement)

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“…They do this by incorporating improvements to such frameworks, such as the pointer generator network, which makes it easier to replicate components of the discussions to the findings, and a hierarchical RNN encoder, which speeds up RNN training with lengthy inputs by a factor of three. Gupta (2022) present an unsupervised method for extractive summarization that is based on semantic similarity and the extraction of keywords. When the Concept Map method and the RAKE method are combined, the result is a generic tool that allows on threshold levels.…”

Section: Literature Surveymentioning

confidence: 99%

Extractive text summarization for biomedical transcripts using deep dense LSTM‐CNN framework

Bedi,

Bala,

Sharma

2023

Expert Systems

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The most recent and precise biological and healthcare knowledge is critical in the current outbreak such as COVID. In today's small world, everyone needs timely and appropriate medical information to prevent contagious diseases. Extraction of important information from medical conversations and dissemination to patients and doctors may benefit in the treatments of doctor tiredness and patient amnesia.ProblemAutomatic text summarizing is essential for gaining great knowledge in any topic in an efficient and productive manner. The material included in health records is vital to our understanding of kind illness and its manifestations. Creating a comprehensive and standard kind of content is becoming an unavoidable and crucial problem in the medical process as a result of the massive amounts of fragmented data created in many sectors.ApproachThe purpose of this study is to employ NLP‐based deep learning algorithms for text summary that perform well on linguistic text summarization data, and then modify/adapt these for biomedical domain‐specific text summarization. This paper provides an approach developed in‐house for condensing ill‐punctuated or unpunctuated discussion transcripts into more intelligible summaries, which combines topic modelling and phrase selection with punctuation restorations. For autonomous synthesis of medical reports from biomedical transcripts, this research proposes using an end‐to‐end summarization technique, Deep Dense Long Short Term Memory Network (LSTM), followed by Convolutional Neural Network (CNN).ResultsExtensive testing, examination, and comparing have demonstrated that this summarizer works well for medical transcript summarization. The suggested approach achieved an average ROUGE score of 93.5% using a single document summary. Furthermore, by comparing new techniques to previous ones, the utility and accuracy of novel strategies would be shown. The results reveal that models trained on ordinary language provide comparable results on a biomedical testing set, with one model outperforming the linguistic test set.

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Section: Literature Surveymentioning

confidence: 99%