The DOI matches its Crossref record; 22 of the 22 references it cites were found in a registry or an index.
A Survey of Text Summarization Extractive Techniques
Abstract
Abstract — Text Summarization is condensing the source text into a shorter version preserving its information content and overall meaning. It is very difficult for human beings to manually summarize large documents of text. Text Summarization methods can be classified into extractive and abstractive summarization. An extractive summarization method consists of selecting important sentences, paragraphs etc. from the original document and concatenating them into shorter form. The importance of sentences is decided based on statistical and linguistic features of sentences. An abstractive summarization method consists of understanding the original text and re-telling it in fewer words. It uses linguistic methods to examine and interpret the text and then to find the new concepts and expressions to best describe it by generating a new shorter text that conveys the most important information from the original text document. In this paper, a Survey of Text Summarization Extractive techniques has been presented. Index Terms—Text Summarization, extractive summary, abstractive summary
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Cites
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Improving Natural Language Processing Tasks with Human Gaze-Guided Neural Attention
Sood, E., Tannert, S., Mueller, P., & Bulling, A. (2020) · arXiv · doi:10.48550/arxiv.2010.07891 · Cited by 42
An Ensemble Approach for Extractive Text Summarization
Singh, P., Chhikara, P., & Singh, J. (2020) · 2020 International Conference on Emerging Trends in Information Technology and Engineering (ic-ETITE) · doi:10.1109/ic-etite47903.2020.95 · Cited by 22
Detecting online child grooming conversation
Gunawan, F. E., Ashianti, L., Candra, S., & Soewito, B. (2016) · 2016 11th International Conference on Knowledge, Information and Creativity Support Systems (KICSS) · doi:10.1109/kicss.2016.7951413 · Cited by 17
Classification of online grooming on chat logs using two term weighting schemes
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Metadata
- Authors
- Gupta, V., & Lehal, G. S.
- Venue
- Journal of Emerging Technologies in Web Intelligence
- Published
- 2010-08-20
- Volume
- 2
- Issue
- 3
- ISSN
- 1798-0461 (ISSN-L)
- OpenAlex
- W2081056190
- Cited by
- 616
- References
- 22
- FWCI
- 17.68
- Type
- journal article
- Language
- en
- Open access
- closed
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