mlsanityData to 14 Oct 2026

Utilizing GPT to Enhance Text Summarization: A Strategy to Minimize Hallucinations

arXiv 2405.04039 ↗First posted 7 May 2024cs.CL cs.AI cs.LGcited by 3

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Abstract (arXiv)

In this research, we uses the DistilBERT model to generate extractive summary and the T5 model to generate abstractive summaries. Also, we generate hybrid summaries by combining both DistilBERT and T5 models. Central to our research is the implementation of GPT-based refining process to minimize the common problem of hallucinations that happens in AI-generated summaries. We evaluate unrefined summaries and, after refining, we also assess refined summaries using a range of traditional and novel metrics, demonstrating marked improvements in the accuracy and reliability of the summaries. Results highlight significant improvements in reducing hallucinatory content, thereby increasing the factual integrity of the summaries.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 14 of its 25 references that are in the map. First posted on arXiv 7 May 2024.

Builds on

14 of 25 references.

References · 14

  1. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,599
  2. 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,170
  3. 2019DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lightercited by 4,307
  4. 2022Survey of Hallucination in Natural Language Generationcited by 2,075
  5. 2019Text Summarization with Pretrained Encoderscited by 556
  6. 2021SummaC: Re-Visiting NLI-based Models for Inconsistency Detection in Summarizationcited by 407
  7. 2020Asking and Answering Questions to Evaluate the Factual Consistency of Summariescited by 378
  8. 2022News Summarization and Evaluation in the Era of GPT-3cited by 327
  9. 2017Faithful to the Original: Fact Aware Neural Abstractive Summarizationcited by 141
  10. 2018Ranking Sentences for Extractive Summarization with Reinforcement Learningcited by 128
  11. 2023Opportunities and Challenges for ChatGPT and Large Language Models in Biomedicine and Healthcited by 118
  12. 2017Text Summarization Techniques: A Brief Surveycited by 59
2 more
  1. 2023Generative Pre-trained Transformer: A Comprehensive Review on Enabling Technologies, Potential Applications, Emerging Challenges, and Future Directionscited by 55
  2. 2021Evaluating the Tradeoff Between Abstractiveness and Factuality in Abstractive Summarizationcited by 16

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Cited by year (2026 to 14 Oct)

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

3 papers.

  1. 2025A Unified Framework with Novel Metrics for Evaluating the Effectiveness of XAI Techniques in LLMscited by 7
  2. 2025Toxicity in Online Platforms and AI Systems: A Survey of Needs, Challenges, Mitigations, and Future Directionscited by 3
  3. 2026Evaluating Multimodal LLMs across Text and Audio Modalities for Accessible Disaster Assistancecited by 0