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Abstractive Summarization: A Survey of the State of the Art
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The focus of automatic text summarization research has exhibited a gradual shift from extractive methods to abstractive methods in recent years, owing in part to advances in neural methods. Originally developed for machine translation, neural methods provide a viable framework for obtaining an abstract representation of the meaning of an input text and generating informative, fluent, and human-like summaries. This paper surveys existing approaches to abstractive summarization, focusing on the recently developed neural approaches.
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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 [88]
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Metadata
- Authors
- Lin, H., & Ng, V.
- Affiliations
- The University of Texas at Dallas (US)
- ORCID
- Venue
- Proceedings of the AAAI Conference on Artificial Intelligence
- Published
- 2019-07-17
- Volume
- 33
- Issue
- 01
- Pages
- 9815-9822
- ISSN
- 2159-5399 (ISSN-L)
- 2374-3468
- OpenAlex
- W2964813902
- Cited by
- 120
- References
- 80
- Citations by year
- 2026: 3 · 2025: 12 · 2024: 23 · 2023: 26 · 2022: 19 · 2021: 21 · 2020: 16
- Type
- conference abstract
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- www.aaai.org/ (unspecified)
- Locations
- Proceedings of the AAAI Conference on Artificial Intelligence (publishedVersion)
- AAAI Publications (The Association for the Advancement of Artificial Intelligence (AAAI)) (submittedVersion)
- Funders
- National Science Foundation (1528037)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Advanced Text Analysis Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Keywords
- abstractive summarization, text summarization, neural summarization, extractive summarization, machine translation, summary generation
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