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A survey on extractive text summarization
Abstract
Text Summarization is the process of obtaining salient information from an authentic text document. In this technique, the extracted information is achieved as a summarized report and conferred as a concise summary to the user. It is very crucial for humans to understand and to describe the content of the text. Text Summarization techniques are classified into abstractive and extractive summarization. The extractive summarization technique focuses on choosing how paragraphs, important sentences, etc produces the original documents in precise form. The implication of sentences is determined based on linguistic and statistical features. In this work, a comprehensive review of extractive text summarization process methods has been ascertained. In this paper, the various techniques, populous benchmarking datasets and challenges of extractive summarization have been reviewed. This paper interprets extractive text summarization methods with a less redundant summary, highly adhesive, coherent and depth information.
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“relevant topics and techniques have already been studied for at least more than two decades in both IR and NLP communities, e.g., extractive and abstractive summarization that generates summary based on retrieved sentences [87, 88] or answer extraction from top retrieved document [89].”
From Foundations of GenIR (2025) · cites this paper as [87]
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Ai, Q., Zhan, J., & Liu, Y. (2025) · arXiv · doi:10.48550/arxiv.2501.02842
“…BART [86], relevant topics and techniques have already been studied for at least more than two decades in both IR and NLP communities, e.g., extractive and abstractive summarization that generates summary based on retrieved sentences [87, 88] or answer extraction from top retrieved document [89].”
Metadata
- Authors
- Moratanch, N., & Chitrakala, S.
- Affiliations
- Anna University, Chennai (IN)
- Venue
- 2017 International Conference on Computer, Communication and Signal Processing (ICCCSP)
- Publisher
- IEEE
- Published
- 2017-01
- Pages
- 1-6
- OpenAlex
- W2623255526
- Cited by
- 265
- References
- 0
- Citations by year
- 2026: 14 · 2025: 26 · 2024: 41 · 2023: 49 · 2022: 55 · 2021: 31 · 2020: 21 · 2019: 21 · 2018: 7
- FWCI
- 6.11
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- International Conference on Computer, Communication and Signal Processing (ICCCSP) (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Advanced Text Analysis Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Keywords
- extractive summarization, abstractive summarization, sentence selection, linguistic features, statistical features, summary coherence
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