Automatic Keyword Extraction for Text Summarization: A Survey
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Abstract (arXiv)
In recent times, data is growing rapidly in every domain such as news, social media, banking, education, etc. Due to the excessiveness of data, there is a need of automatic summarizer which will be capable to summarize the data especially textual data in original document without losing any critical purposes. Text summarization is emerged as an important research area in recent past. In this regard, review of existing work on text summarization process is useful for carrying out further research. In this paper, recent literature on automatic keyword extraction and text summarization are presented since text summarization process is highly depend on keyword extraction. This literature includes the discussion about different methodology used for keyword extraction and text summarization. It also discusses about different databases used for text summarization in several domains along with evaluation matrices. Finally, it discusses briefly about issues and research challenges faced by researchers along with future direction.
Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). First posted on arXiv 11 Apr 2017.
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Citing papers per year: 2021: 1; 2022: 3; 2023: 2; 2024: 1.
7 papers.
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- 2024Self-Improving Customer Review Response Generation Based on LLMs6 May 2024
- 2023JobHam-place with smart recommend job options and candidate filtering options31 Mar 2023
- 2023A comprehensive review of automatic text summarization techniques: method, data, evaluation and coding4 Jan 2023
- 2022Open-world Story Generation with Structured Knowledge Enhancement: A Comprehensive Survey9 Dec 2022
- 2022Detecting Emerging Technologies and their Evolution using Deep Learning and Weak Signal Analysis11 May 2022
- 2022Using virtual edges to extract keywords from texts modeled as complex networks4 May 2022
- 2021Can Social Ontological Knowledge Representations be Measured Using Machine Learning?27 Dec 2021