mlsanityData to 7 Oct 2026

Abstractive Summarization of Large Document Collections Using GPT

arXiv 2310.05690 ↗First posted 9 Oct 2023cs.AIcited by 6

Not on the map. Why?

Abstract (arXiv)

This paper proposes a method of abstractive summarization designed to scale to document collections instead of individual documents. Our approach applies a combination of semantic clustering, document size reduction within topic clusters, semantic chunking of a cluster's documents, GPT-based summarization and concatenation, and a combined sentiment and text visualization of each topic to support exploratory data analysis. Statistical comparison of our results to existing state-of-the-art systems BART, BRIO, PEGASUS, and MoCa using ROGUE summary scores showed statistically equivalent performance with BART and PEGASUS on the CNN/Daily Mail test dataset, and with BART on the Gigaword test dataset. This finding is promising since we view document collection summarization as more challenging than individual document summarization. We conclude with a discussion of how issues of scale are

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 21 of its 94 references that are in the map. First posted on arXiv 9 Oct 2023.

Builds on

21 of 94 references.

References · 21

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2020Language Models are Few-Shot Learnerscited by 32,199
  3. 2023LLaMA: Open and Efficient Foundation Language Modelscited by 13,958
  4. 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,049
  5. 2019BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehensioncited by 5,874
  6. 2018UMAP: Uniform Manifold Approximation and Projection for Dimension Reductioncited by 2,841
  7. 2017Billion-scale similarity search with GPUscited by 2,477
  8. 2022BLOOM: A 176B-Parameter Open-Access Multilingual Language Modelcited by 1,968
  9. 2019PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarizationcited by 1,100
  10. 2016Abstractive Text Summarization Using Sequence-to-Sequence RNNs and Beyondcited by 809
  11. 2015A Neural Attention Model for Abstractive Sentence Summarizationcited by 394
  12. 2019MASS: Masked Sequence to Sequence Pre-training for Language Generationcited by 345
9 more
  1. 2013Crowdsourcing a Word-Emotion Association Lexiconcited by 193
  2. 2022BRIO: Bringing Order to Abstractive Summarizationcited by 161
  3. 2018Deep Learning for Sentiment Analysis : A Surveycited by 139
  4. 2016Stance and Sentiment in Tweetscited by 62
  5. 2018Toward Abstractive Summarization Using Semantic Representationscited by 43
  6. 2020Sentiment Analysis: Automatically Detecting Valence, Emotions, and Other Affectual States from Textcited by 26
  7. 2021Uniform Manifold Approximation and Projection (UMAP) and its Variants: Tutorial and Surveycited by 10
  8. 2022Momentum Calibration for Text Generationcited by 8
  9. 2022A Survey on Neural Abstractive Summarization Methods and Factual Consistency of Summarizationcited by 3

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 3; 2025: 2; 2026: 1.

6 papers.

  1. 2025Document Intelligence in the Era of Large Language Models: A Surveycited by 5
  2. 2024Assisting humans in complex comparisons: automated information comparison at scalecited by 0
  3. 2024Automated Multi-Language to English Machine Translation Using Generative Pre-Trained Transformerscited by 0
  4. 2024DaVinci at SemEval-2024 Task 9: Few-shot prompting GPT-3.5 for Unconventional Reasoningcited by 0
  5. 2026Large Language Model and Formal Concept Analysis: a comparative study for Topic Modelingcited by 0
  6. 2025Althea: The Fact-Checking--Metalearning Tradeoff in AI-Assisted Verificationcited by 0