mlsanityData to 7 Oct 2026

Bayesian Active Summarization

arXiv 2110.04480 ↗First posted 9 Oct 2021cs.CLcited by 5

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Abstract (arXiv)

Bayesian Active Learning has had significant impact to various NLP problems, but nevertheless it's application to text summarization has been explored very little. We introduce Bayesian Active Summarization (BAS), as a method of combining active learning methods with state-of-the-art summarization models. Our findings suggest that BAS achieves better and more robust performance, compared to random selection, particularly for small and very small data annotation budgets. Using BAS we showcase it is possible to leverage large summarization models to effectively solve real-world problems with very limited annotated data.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 23 of its 36 references that are in the map. First posted on arXiv 9 Oct 2021.

Builds on

23 of 36 references.

References · 23

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  9. 2018Don't Give Me the Details, Just the Summary! Topic-Aware Convolutional Neural Networks for Extreme Summarizationcited by 1,308
  10. 2017A Survey on Deep Learning in Medical Image Analysiscited by 1,182
  11. 2019PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarizationcited by 1,100
  12. 2015Teaching Machines to Read and Comprehendcited by 1,039
11 more
  1. 2017Deep Bayesian Active Learning with Image Datacited by 812
  2. 2019BatchBALD: Efficient and Diverse Batch Acquisition for Deep Bayesian Active Learningcited by 428
  3. 2018Newsroom: A Dataset of 1.3 Million Summaries with Diverse Extractive Strategiescited by 292
  4. 2017Deep Active Learning for Named Entity Recognitioncited by 108
  5. 2018Deep Bayesian Active Learning for Natural Language Processing: Results of a Large-Scale Empirical Studycited by 90
  6. 2020Wat zei je? Detecting Out-of-Distribution Translations with Variational Transformerscited by 31
  7. 2018Active Learning with Partial Feedbackcited by 27
  8. 2020Fine-tuning BERT for Low-Resource Natural Language Understanding via Active Learningcited by 25
  9. 2018On the Importance of Strong Baselines in Bayesian Deep Learningcited by 12
  10. 2021Should We Trust This Summary? Bayesian Abstractive Summarization to The Rescuecited by 11
  11. 2020LTP: A New Active Learning Strategy for CRF-Based Named Entity Recognitioncited by 10

Built on by

Cited by year

Citing papers per year: 2023: 2; 2024: 2; 2025: 1.

5 papers.

  1. 2023Active Learning for Abstractive Text Summarizationcited by 6
  2. 2023Active Learning for Natural Language Generationcited by 3
  3. 2024Can We Trust the Performance Evaluation of Uncertainty Estimation Methods in Text Summarization?cited by 3
  4. 2025DUAL: Diversity and Uncertainty Active Learning for Text Summarizationcited by 3
  5. 2024Combining X-Vectors and Bayesian Batch Active Learning: Two-Stage Active Learning Pipeline for Speech Recognitioncited by 0