Improving the Performance of the Extractive Text Summarization by a Novel Topic Modeling and Sentence Embedding Technique using SBERT

Suryadjaja, Paulus Setiawan; Mandala, Rila

doi:10.1109/icaicta53211.2021.9640295

“…This is done to provide a summary of the abstractive approach. This approach is more complex, but it has the potential to generate summaries that are both more useful and more similar to summaries produced by humans [5].…”

Section: Introductionmentioning

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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“…This is done to provide a summary of the abstractive approach. This approach is more complex, but it has the potential to generate summaries that are both more useful and more similar to summaries produced by humans [5].…”

Section: Introductionmentioning

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