Semantic Summarizer
By: Basit, Farooq
.
Contributor(s): Amjad Hussain.
Publisher: New Delhi STM Journals 2019Edition: Vol.6(3), Sep-Dec.Description: 1-8p.Subject(s): Computer EngineeringOnline resources: Click here
In:
Journal of artificial intelligence research and advances (JoAIRA)Summary: Abstract: This work presents the survey of the existing approaches used for automatic text summarization. Automatic text summarization technique belongs to the natural language processing area, and is applied on the source document to produce its compact version that preserves its aggregate meaning and key concepts. On a broader scale, approaches for text summarization task are classified into two categories: (1) abstractive and (1) extractive. In abstractive summarization, main contents of the input text are paraphrased possibly using vocabulary that is not present in the source document, while as in extractive summarization, output summary is a subset of the input text and is generated by using sentence ranking technique. In this paper, the main ideas behind the existing methods used for abstractive and extractive summarization are discussed broadly. Comparative study on these methods is also highlighted.
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School of Engineering & Technology Archieval Section | Not for loan | 2021120 |
Abstract: This work presents the survey of the existing approaches used for automatic text summarization. Automatic text summarization technique belongs to the natural language processing area, and is applied on the source document to produce its compact version that preserves its aggregate meaning and key concepts. On a broader scale, approaches for text summarization task are classified into two categories: (1) abstractive and (1) extractive. In abstractive summarization, main contents of the input text are paraphrased possibly using vocabulary that is not present in the source document, while as in extractive summarization, output summary is a subset of the input text and is generated by using sentence ranking technique. In this paper, the main ideas behind the existing methods used for abstractive and extractive summarization are discussed broadly. Comparative study on these methods is also highlighted.
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