Multilayer CARU Model for Text Summarization

Im, Sio‐Kei; Chan, Ka‐Hou

doi:10.1109/bigcomp57234.2023.00098

“…The process of comparing the text and creating the outline is made more difficult by the use of pre-trained models that are based on transformer architecture. The findings acquired from the machine learning models are evaluated and compared with the help of the BBC news dataset for this study investigation [29]. They have completed an analysis of several methods for automatically summarising text.…”

Section: Computational Intelligence and Machine Learning E-issn: 2582...mentioning

confidence: 99%

A Review of Text Summarization Techniques Using NLP

Aggarwal

2023

CIML

0

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Techniques that employ natural language processing (NLP), often known as text summarizing, automatically construct summaries of extensive texts. Extractive and abstractive summarization are two main categories that may be used to classify these methods. In extractive summarizing, the most significant lines or phrases from a text are isolated and used to generate a summary. On the other hand, in abstractive summarization, a summary is generated that is clear, short, and accurate in its representation of the text's primary concepts. NLP methods like sentence segmentation, part-of-speech tagging, named entity recognition, and semantic analysis are used in generating a summary from a text and locating and extracting relevant information from the text. Text summarizing is a subject that has received a significant amount of study and has applications in various fields, including the summation of news articles, documents, and emails, among other things.

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“…The process of comparing the text and creating the outline is made more difficult by the use of pre-trained models that are based on transformer architecture. The findings acquired from the machine learning models are evaluated and compared with the help of the BBC news dataset for this study investigation [29]. They have completed an analysis of several methods for automatically summarising text.…”

Section: Computational Intelligence and Machine Learning E-issn: 2582...mentioning

confidence: 99%

Untitled

2023

CIML

0

View full text Add to dashboard Cite

Techniques that employ natural language processing (NLP), often known as text summarizing, automatically construct summaries of extensive texts. Extractive and abstractive summarization are two main categories that may be used to classify these methods. In extractive summarizing, the most significant lines or phrases from a text are isolated and used to generate a summary. On the other hand, in abstractive summarization, a summary is generated that is clear, short, and accurate in its representation of the text's primary concepts. NLP methods like sentence segmentation, part-of-speech tagging, named entity recognition, and semantic analysis are used in generating a summary from a text and locating and extracting relevant information from the text. Text summarizing is a subject that has received a significant amount of study and has applications in various fields, including the summation of news articles, documents, and emails, among other things.

show abstract