mlsanityData to 14 Oct 2026

A Survey on Neural Abstractive Summarization Methods and Factual Consistency of Summarization

arXiv 2204.09519 ↗First posted 20 Apr 2022cs.CLcited by 3

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Abstract (arXiv)

Automatic summarization is the process of shortening a set of textual data computationally, to create a subset (a summary) that represents the most important pieces of information in the original text. Existing summarization methods can be roughly divided into two types: extractive and abstractive. An extractive summarizer explicitly selects text snippets (words, phrases, sentences, etc.) from the source document, while an abstractive summarizer generates novel text snippets to convey the most salient concepts prevalent in the source.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 24 of its 36 references that are in the map. First posted on arXiv 20 Apr 2022.

Builds on

24 of 36 references.

References · 24

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12 more
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Cited by year

Citing papers per year: 2023: 1; 2024: 2.

3 papers.

  1. 2024A Systematic Survey of Text Summarization: From Statistical Methods to Large Language Modelscited by 52
  2. 2023Abstractive Summarization of Large Document Collections Using GPTcited by 6
  3. 2024Enhancing News Summarization with ELearnFit through Efficient In-Context Learning and Efficient Fine-Tuningcited by 0