mlsanityData to 7 Oct 2026

An Analysis of Abstractive Text Summarization Using Pre-trained Models

arXiv 2303.12796 ↗First posted 25 Feb 2023cs.CL cs.AI cs.LGcited by 7

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Abstract (arXiv)

People nowadays use search engines like Google, Yahoo, and Bing to find information on the Internet. Due to explosion in data, it is helpful for users if they are provided relevant summaries of the search results rather than just links to webpages. Text summarization has become a vital approach to help consumers swiftly grasp vast amounts of information.In this paper, different pre-trained models for text summarization are evaluated on different datasets. Specifically, we have used three different pre-trained models, namely, google/pegasus-cnn-dailymail, T5-base, facebook/bart-large-cnn. We have considered three different datasets, namely, CNN-dailymail, SAMSum and BillSum to get the output from the above three models. The pre-trained models are compared over these different datasets, each of 2000 examples, through ROUGH and BLEU metrics.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 14 of its 22 references that are in the map. First posted on arXiv 25 Feb 2023.

Builds on

14 of 22 references.

References · 14

  1. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,491
  2. 2019BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehensioncited by 5,874
  3. 2014Neural Machine Translation by Jointly Learning to Align and Translatecited by 4,530
  4. 2014Sequence to Sequence Learning with Neural Networkscited by 2,974
  5. 2017Get To The Point: Summarization with Pointer-Generator Networkscited by 1,281
  6. 2019PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarizationcited by 1,100
  7. 2015Teaching Machines to Read and Comprehendcited by 1,039
  8. 2015Effective Approaches to Attention-based Neural Machine Translationcited by 1,024
  9. 2016Abstractive Text Summarization Using Sequence-to-Sequence RNNs and Beyondcited by 809
  10. 2019SAMSum Corpus: A Human-annotated Dialogue Dataset for Abstractive Summarizationcited by 530
  11. 2019BillSum: A Corpus for Automatic Summarization of US Legislationcited by 117
  12. 2018Neural Abstractive Text Summarization with Sequence-to-Sequence Modelscited by 39
2 more
  1. 2018Global Encoding for Abstractive Summarizationcited by 16
  2. 2020Abstractive Text Summarization based on Language Model Conditioning and Locality Modelingcited by 9

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Cited by year

Citing papers per year: 2023: 3; 2024: 1; 2025: 3.

7 papers.

  1. 2023Hallucination Reduction in Long Input Text Summarizationcited by 5
  2. 2025Evaluating LLMs and Pre-trained Models for Text Summarization Across Diverse Datasetscited by 3
  3. 2024Analysis of Multidomain Abstractive Summarization Using Salience Allocationcited by 2
  4. 2025How Green are Neural Language Models? Analyzing Energy Consumption in Text Summarization Fine-tuningcited by 2
  5. 2023Abstractive Text Summarization for Resumes With Cutting Edge NLP Transformers and LSTMcited by 0
  6. 2023Controlled Randomness Improves the Performance of Transformer Modelscited by 0
  7. 2025Comparative Analysis of Abstractive Summarization Models for Clinical Radiology Reportscited by 0