A Concise Review on Automatic Text Summarization

Jani, Dishank; Patel, Nehal; Yadav, Hemant; Suthar, Sanket B.; Patel, Sandip

doi:10.1007/978-981-16-9447-9_40

Cited by 4 publications

(2 citation statements)

References 26 publications

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“…In the former, summaries are produced by selecting a few tokens from the original text without modifying them. In the latter, the input text is paraphrased more coherently using intermediate conceptual representations [14], [44]- [46]. Abstract summaries are qualitatively close to human-written ones.…”

Section: B Text Summarizationmentioning

confidence: 95%

ReQuEST: A Small-Scale Multi-Task Model for Community Question-Answering Systems

Aftabi,

Seyyedi,

Maleki

et al. 2024

IEEE Access

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The burgeoning popularity of community question-answering platforms as an informationseeking strategy has prompted researchers to look for ways to save response time and effort, among which question entailment recognizing, question summarizing, and question tagging are prominent. However, none has investigated the implicit relations between these tasks and the benefits their interaction could provide. In this study, ReQuEST, a novel multi-task model based on bidirectional auto-regressive transformers (BART), is introduced to recognize question entailment, summarize questions respecting given queries, and tag questions with primary topics, simultaneously. ReQuEST comprises one shared encoder representing input sequences, two half-shared decoders providing intermediate presentations, and three task-specific heads producing summaries, tags, and entailed questions. A lightweight fine-tuning technique and a weighted loss function help us learn model parameters efficiently. With roughly 187k learning parameters, ReQuEST is almost half the size of BARTlarge and is two-thirds smaller than its multi-task counterparts. Empirical experiments on standard summarization datasets reveal that ReQuEST outperforms competitors on Debatepedia with a Rouge-L of 46.77 and has persuasive performance with a Rouge-L of 37.37 on MeQSum. On MediQA-RQE as a medical benchmark for entailment recognition, ReQuEST is also comparable in accuracy with state-of-the-art systems without being pre-trained on domain-specific datasets.

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Section: B Text Summarizationmentioning

confidence: 95%

ReQuEST: A Small-Scale Multi-Task Model for Community Question-Answering Systems

Aftabi,

Seyyedi,

Maleki

et al. 2024

IEEE Access

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show abstract

“…The expansion of the internet has resulted in an excess of multi-sourced material accessible for public consumption in recent years. In many situations, this amount of easily accessible material requires an efficient summary in order to be helpful for a variety of purposes (Jani et al, 2022). The goal of summarization would be to reduce the length of a textual document while preserving the most crucial points as well as the real message of the source.…”

Section: Introductionmentioning

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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