# A Systematic Survey of Text Summarization: From Statistical Methods to Large Language Models

arXiv: https://arxiv.org/abs/2406.11289 · HTML: https://mlsanity.com/paper/2406.11289/

## Key facts

- Cited by 52 papers in the map (data through 14 Oct 2026).
- Builds on 194 of its 285 references that are in the map.
- First posted on arXiv 17 Jun 2024.

## Builds on (most cited first, 30 of 194)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [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)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://mlsanity.com/paper/1910.01108/) (2019)
- [Deep reinforcement learning from human preferences](https://mlsanity.com/paper/1706.03741/) (2017)
- [QLoRA: Efficient Finetuning of Quantized LLMs](https://mlsanity.com/paper/2305.14314/) (2023)
- [Longformer: The Long-Document Transformer](https://mlsanity.com/paper/2004.05150/) (2020)
- [Scaling Instruction-Finetuned Language Models](https://mlsanity.com/paper/2210.11416/) (2022)
- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [TruthfulQA: Measuring How Models Mimic Human Falsehoods](https://mlsanity.com/paper/2109.07958/) (2021)
- [ALBERT: A Lite BERT for Self-supervised Learning of Language Representations](https://mlsanity.com/paper/1909.11942/) (2019)
- [Learning to summarize from human feedback](https://mlsanity.com/paper/2009.01325/) (2020)
- [G-Eval: NLG Evaluation using GPT-4 with Better Human Alignment](https://mlsanity.com/paper/2303.16634/) (2023)
- [Survey of Hallucination in Natural Language Generation](https://mlsanity.com/paper/2202.03629/) (2022)
- [SmoothQuant: Accurate and Efficient Post-Training Quantization for Large Language Models](https://mlsanity.com/paper/2211.10438/) (2022)
- [Don't Give Me the Details, Just the Summary! Topic-Aware Convolutional Neural Networks for Extreme Summarization](https://mlsanity.com/paper/1808.08745/) (2018)
- [Get To The Point: Summarization with Pointer-Generator Networks](https://mlsanity.com/paper/1704.04368/) (2017)

## Built on by (most cited first, 30 of 52)

- [WebWeaver: Structuring Web-Scale Evidence with Dynamic Outlines for Open-Ended Deep Research](https://mlsanity.com/paper/2509.13312/) (2025)
- [The Rogue Scalpel: Activation Steering Compromises LLM Safety](https://mlsanity.com/paper/2509.22067/) (2025)
- [A Survey of Sustainability in Large Language Models: Applications, Economics, and Challenges](https://mlsanity.com/paper/2412.04782/) (2024)
- [HiBench: Benchmarking LLMs Capability on Hierarchical Structure Reasoning](https://mlsanity.com/paper/2503.00912/) (2025)
- [Teaching Large Language Models to Maintain Contextual Faithfulness via Synthetic Tasks and Reinforcement Learning](https://mlsanity.com/paper/2505.16483/) (2025)
- [HumanLLM: Towards Personalized Understanding and Simulation of Human Nature](https://mlsanity.com/paper/2601.15793/) (2026)
- [ESPERANTO: Evaluating Synthesized Phrases to Enhance Robustness in AI Detection for Text Origination](https://mlsanity.com/paper/2409.14285/) (2024)
- [Document Summarization with Conformal Importance Guarantees](https://mlsanity.com/paper/2509.20461/) (2025)
- [Semantic Caching of Contextual Summaries for Efficient Question-Answering with Language Models](https://mlsanity.com/paper/2505.11271/) (2025)
- [Memorization in Large Language Models in Medicine: Prevalence, Characteristics, and Implications](https://mlsanity.com/paper/2509.08604/) (2025)
- [KVSwap: Disk-aware KV Cache Offloading for Long-Context On-device Inference](https://mlsanity.com/paper/2511.11907/) (2025)
- [Recursive Decomposition of Logical Thoughts: Framework for Superior Reasoning and Knowledge Propagation in Large Language Models](https://mlsanity.com/paper/2501.02026/) (2025)
- [OpinioRAG: Towards Generating User-Centric Opinion Highlights from Large-scale Online Reviews](https://mlsanity.com/paper/2509.00285/) (2025)
- [Evaluating LLMs and Pre-trained Models for Text Summarization Across Diverse Datasets](https://mlsanity.com/paper/2502.19339/) (2025)
- [KVCompose: Efficient Structured KV Cache Compression with Composite Tokens](https://mlsanity.com/paper/2509.05165/) (2025)
- [Optimizing Long-Form Clinical Text Generation with Claim-Based Rewards](https://mlsanity.com/paper/2510.02338/) (2025)
- [Balancing Rewards in Text Summarization: Multi-Objective Reinforcement Learning via HyperVolume Optimization](https://mlsanity.com/paper/2510.19325/) (2025)
- [CoT-X: An Adaptive Framework for Cross-Model Chain-of-Thought Transfer and Optimization](https://mlsanity.com/paper/2511.05747/) (2025)
- [MoDora: Tree-Based Semi-Structured Document Analysis System](https://mlsanity.com/paper/2602.23061/) (2026)
- [Evaluating the Effectiveness of Large Language Models in Automated News Article Summarization](https://mlsanity.com/paper/2502.17136/) (2025)
- [RCPU: Rotation-Constrained Error Compensation for Structured Pruning of Large Language Models](https://mlsanity.com/paper/2510.07782/) (2025)
- [TSM-Bench: Detecting LLM-Generated Text in Real-World Wikipedia Editing Practices](https://mlsanity.com/paper/2605.31113/) (2026)
- [Evaluating book summaries from internal knowledge in Large Language Models: a cross-model and semantic consistency approach](https://mlsanity.com/paper/2503.21613/) (2025)
- [News is More than a Collection of Facts: Moral Frame Preserving News Summarization](https://mlsanity.com/paper/2504.00657/) (2025)
- [From Text to Graph: Leveraging Graph Neural Networks for Enhanced Explainability in NLP](https://mlsanity.com/paper/2504.02064/) (2025)
- [Ask, Retrieve, Summarize: A Modular Pipeline for Scientific Literature Summarization](https://mlsanity.com/paper/2505.16349/) (2025)
- [LLMs are Introvert](https://mlsanity.com/paper/2507.05638/) (2025)
- [MAGIC-Enhanced Keyword Prompting for Zero-Shot Audio Captioning with CLIP Models](https://mlsanity.com/paper/2509.12591/) (2025)
- [Efficient Extractive Text Summarization for Online News Articles Using Machine Learning](https://mlsanity.com/paper/2509.15614/) (2025)
- [End-to-End Transformer Acceleration Through Processing-in-Memory Architectures](https://mlsanity.com/paper/2601.14260/) (2025)

## Abstract (arXiv)

Text summarization research has undergone several significant transformations with the advent of deep neural networks, pre-trained language models (PLMs), and recent large language models (LLMs). This survey thus provides a comprehensive review of the research progress and evolution in text summarization through the lens of these paradigm shifts. It is organized into two main parts: (1) a detailed overview of datasets, evaluation metrics, and summarization methods before the LLM era, encompassing traditional statistical methods, deep learning approaches, and PLM fine-tuning techniques, and (2) the first detailed examination of recent advancements in benchmarking, modeling, and evaluating summarization in the LLM era. By synthesizing existing literature and presenting a cohesive overview, this survey also discusses research trends, open challenges, and proposes promising research directions in summarization, aiming to guide researchers through the evolving landscape of summarization research.