# SummIt: Iterative Text Summarization via ChatGPT

arXiv: https://arxiv.org/abs/2305.14835 · HTML: https://mlsanity.com/paper/2305.14835/

## Key facts

- Cited by 54 papers in the map (data through 14 Oct 2026).
- Builds on 34 of its 46 references that are in the map.
- First posted on arXiv 24 May 2023.

## Builds on (most cited first, 30 of 34)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [Dense Passage Retrieval for Open-Domain Question Answering](https://mlsanity.com/paper/2004.04906/) (2020)
- [Self-Refine: Iterative Refinement with Self-Feedback](https://mlsanity.com/paper/2303.17651/) (2023)
- [G-Eval: NLG Evaluation using GPT-4 with Better Human Alignment](https://mlsanity.com/paper/2303.16634/) (2023)
- [Don't Give Me the Details, Just the Summary! Topic-Aware Convolutional Neural Networks for Extreme Summarization](https://mlsanity.com/paper/1808.08745/) (2018)
- [Teaching Machines to Read and Comprehend](https://mlsanity.com/paper/1506.03340/) (2015)
- [Abstractive Text Summarization Using Sequence-to-Sequence RNNs and Beyond](https://mlsanity.com/paper/1602.06023/) (2016)
- [Evaluating the Factual Consistency of Abstractive Text Summarization](https://mlsanity.com/paper/1910.12840/) (2019)
- [Text Summarization with Pretrained Encoders](https://mlsanity.com/paper/1908.08345/) (2019)
- [Benchmarking Large Language Models for News Summarization](https://mlsanity.com/paper/2301.13848/) (2023)
- [GPTScore: Evaluate as You Desire](https://mlsanity.com/paper/2302.04166/) (2023)
- [News Summarization and Evaluation in the Era of GPT-3](https://mlsanity.com/paper/2209.12356/) (2022)
- [Generating Sequences by Learning to Self-Correct](https://mlsanity.com/paper/2211.00053/) (2022)
- [Extractive Summarization as Text Matching](https://mlsanity.com/paper/2004.08795/) (2020)
- [Graph-Bert: Only Attention is Needed for Learning Graph Representations](https://mlsanity.com/paper/2001.05140/) (2020)
- [Evaluating Factuality in Generation with Dependency-level Entailment](https://mlsanity.com/paper/2010.05478/) (2020)
- [Neural Summarization by Extracting Sentences and Words](https://mlsanity.com/paper/1603.07252/) (2016)
- [RL4F: Generating Natural Language Feedback with Reinforcement Learning for Repairing Model Outputs](https://mlsanity.com/paper/2305.08844/) (2023)
- [PEER: A Collaborative Language Model](https://mlsanity.com/paper/2208.11663/) (2022)
- [Parallel Iterative Edit Models for Local Sequence Transduction](https://mlsanity.com/paper/1910.02893/) (2019)
- [Knowledge Graph-Augmented Abstractive Summarization with Semantic-Driven Cloze Reward](https://mlsanity.com/paper/2005.01159/) (2020)
- [Extractive Summarization via ChatGPT for Faithful Summary Generation](https://mlsanity.com/paper/2304.04193/) (2023)
- [FRUIT: Faithfully Reflecting Updated Information in Text](https://mlsanity.com/paper/2112.08634/) (2021)
- [On Improving Summarization Factual Consistency from Natural Language Feedback](https://mlsanity.com/paper/2212.09968/) (2022)
- [Text Editing by Command](https://mlsanity.com/paper/2010.12826/) (2020)
- [Learning to Model Editing Processes](https://mlsanity.com/paper/2205.12374/) (2022)
- [HEGEL: Hypergraph Transformer for Long Document Summarization](https://mlsanity.com/paper/2210.04126/) (2022)

## Built on by (most cited first, 30 of 54)

- [Parameter-Efficient Fine-Tuning for Large Models: A Comprehensive Survey](https://mlsanity.com/paper/2403.14608/) (2024)
- [When Search Engine Services meet Large Language Models: Visions and Challenges](https://mlsanity.com/paper/2407.00128/) (2024)
- [Self-Verification Improves Few-Shot Clinical Information Extraction](https://mlsanity.com/paper/2306.00024/) (2023)
- [A Systematic Survey of Text Summarization: From Statistical Methods to Large Language Models](https://mlsanity.com/paper/2406.11289/) (2024)
- [Large Language Models Hallucination: A Comprehensive Survey](https://mlsanity.com/paper/2510.06265/) (2025)
- [LeanContext: Cost-Efficient Domain-Specific Question Answering Using LLMs](https://mlsanity.com/paper/2309.00841/) (2023)
- [Leveraging Large Language Models for NLG Evaluation: Advances and Challenges](https://mlsanity.com/paper/2401.07103/) (2024)
- [HtmlRAG: HTML is Better Than Plain Text for Modeling Retrieved Knowledge in RAG Systems](https://mlsanity.com/paper/2411.02959/) (2024)
- [On Context Utilization in Summarization with Large Language Models](https://mlsanity.com/paper/2310.10570/) (2023)
- [PsycoLLM: Enhancing LLM for Psychological Understanding and Evaluation](https://mlsanity.com/paper/2407.05721/) (2024)
- [Tree Prompting: Efficient Task Adaptation without Fine-Tuning](https://mlsanity.com/paper/2310.14034/) (2023)
- [GhostWriter: Augmenting Collaborative Human-AI Writing Experiences Through Personalization and Agency](https://mlsanity.com/paper/2402.08855/) (2024)
- [Build the web for agents, not agents for the web](https://mlsanity.com/paper/2506.10953/) (2025)
- [Prompt Chaining or Stepwise Prompt? Refinement in Text Summarization](https://mlsanity.com/paper/2406.00507/) (2024)
- [Abstractive Text Summarization: State of the Art, Challenges, and Improvements](https://mlsanity.com/paper/2409.02413/) (2024)
- [OpenAsp: A Benchmark for Multi-document Open Aspect-based Summarization](https://mlsanity.com/paper/2312.04440/) (2023)
- [Better to Ask in English: Evaluation of Large Language Models on English, Low-resource and Cross-Lingual Settings](https://mlsanity.com/paper/2410.13153/) (2024)
- [A Comparative Study of Quality Evaluation Methods for Text Summarization](https://mlsanity.com/paper/2407.00747/) (2024)
- [FollowEval: A Multi-Dimensional Benchmark for Assessing the Instruction-Following Capability of Large Language Models](https://mlsanity.com/paper/2311.09829/) (2023)
- [Low-Resource Machine Translation through Retrieval-Augmented LLM Prompting: A Study on the Mambai Language](https://mlsanity.com/paper/2404.04809/) (2024)
- [HyGen: Efficient LLM Serving via Elastic Online-Offline Request Co-location](https://mlsanity.com/paper/2501.14808/) (2025)
- [Knowledge is Power: Harnessing Large Language Models for Enhanced Cognitive Diagnosis](https://mlsanity.com/paper/2502.05556/) (2025)
- [DLoRA: Distributed Parameter-Efficient Fine-Tuning Solution for Large Language Model](https://mlsanity.com/paper/2404.05182/) (2024)
- [What's Wrong? Refining Meeting Summaries with LLM Feedback](https://mlsanity.com/paper/2407.11919/) (2024)
- [PoetryDiffusion: Towards Joint Semantic and Metrical Manipulation in Poetry Generation](https://mlsanity.com/paper/2306.08456/) (2023)
- [XATU: A Fine-grained Instruction-based Benchmark for Explainable Text Updates](https://mlsanity.com/paper/2309.11063/) (2023)
- [ORCHID: A Chinese Debate Corpus for Target-Independent Stance Detection and Argumentative Dialogue Summarization](https://mlsanity.com/paper/2410.13667/) (2024)
- [Exploring Large Language Models for Semantic Analysis and Categorization of Android Malware](https://mlsanity.com/paper/2501.04848/) (2025)
- [Wi-Chat: Large Language Model Powered Wi-Fi Sensing](https://mlsanity.com/paper/2502.12421/) (2025)
- [Analyzing Regional Impacts of Climate Change using Natural Language Processing Techniques](https://mlsanity.com/paper/2401.06817/) (2024)

## Abstract (arXiv)

Text summarization systems have made significant progress in recent years, but typically generate summaries in one single step. However, the one-shot summarization setting is sometimes inadequate, as the generated summary may contain hallucinations or overlook essential details related to the reader's interests. This paper addresses this limitation by proposing SummIt, an iterative text summarization framework based on large language models like ChatGPT. Our framework enables the model to refine the generated summary iteratively through self-evaluation and feedback, resembling humans' iterative process when drafting and revising summaries. Furthermore, we explore the potential benefits of integrating knowledge and topic extractors into the framework to enhance summary faithfulness and controllability. We automatically evaluate the performance of our framework on three benchmark summarization datasets. We also conduct a human evaluation to validate the effectiveness of the iterative refinements and identify a potential issue of over-correction.