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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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Omar, A., Altohami, W. M. A., & Hamouda, W. I. (2023) · Cogent Arts and Humanities · doi:10.1080/23311983.2023.2185968 · Cited by 1
Bodrunova, S. S. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-35915-6_4 · Cited by 1
Neural Abstractive Summarization: A Brief Survey
Lu, Q., Zhang, H., Wang, W., Liu, D., & Huang, X. (2023) · International Conference on Computer Communication and Artificial Intelligence (CCAI) · doi:10.1109/ccai57533.2023.10201274 · Cited by 1
A Comprehensive Survey on Text Mining From Theory to Practice
Zare, D., Sanz, L. F., Pospelova, V., & López-Baldominos, I. (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3693-9606-3.ch009 · Cited by 1
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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