# Abstractive Text Summarization: State of the Art, Challenges, and Improvements

arXiv: https://arxiv.org/abs/2409.02413 · HTML: https://mlsanity.com/paper/2409.02413/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Builds on 93 of its 224 references that are in the map.
- First posted on arXiv 4 Sep 2024.

## Builds on (most cited first, 30 of 93)

- [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)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (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)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [Survey of Hallucination in Natural Language Generation](https://mlsanity.com/paper/2202.03629/) (2022)
- [CIDEr: Consensus-based Image Description Evaluation](https://mlsanity.com/paper/1411.5726/) (2014)
- [Towards A Rigorous Science of Interpretable Machine Learning](https://mlsanity.com/paper/1702.08608/) (2017)
- [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)
- [A Survey on Evaluation of Large Language Models](https://mlsanity.com/paper/2307.03109/) (2023)
- [PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization](https://mlsanity.com/paper/1912.08777/) (2019)
- [Pointer Networks](https://mlsanity.com/paper/1506.03134/) (2015)
- [A Multitask, Multilingual, Multimodal Evaluation of ChatGPT on Reasoning, Hallucination, and Interactivity](https://mlsanity.com/paper/2302.04023/) (2023)
- [Abstractive Text Summarization Using Sequence-to-Sequence RNNs and Beyond](https://mlsanity.com/paper/1602.06023/) (2016)
- [Improving Neural Machine Translation Models with Monolingual Data](https://mlsanity.com/paper/1511.06709/) (2015)
- [CTRL: A Conditional Transformer Language Model for Controllable Generation](https://mlsanity.com/paper/1909.05858/) (2019)
- [Unified Language Model Pre-training for Natural Language Understanding and Generation](https://mlsanity.com/paper/1905.03197/) (2019)
- [How multilingual is Multilingual BERT?](https://mlsanity.com/paper/1906.01502/) (2019)
- [A Survey on Knowledge Graphs: Representation, Acquisition and Applications](https://mlsanity.com/paper/2002.00388/) (2020)
- [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)
- [Self-critical Sequence Training for Image Captioning](https://mlsanity.com/paper/1612.00563/) (2016)
- [Google's Multilingual Neural Machine Translation System: Enabling Zero-Shot Translation](https://mlsanity.com/paper/1611.04558/) (2016)

## Built on by (most cited first, 17 of 17)

- [A Unified Framework with Novel Metrics for Evaluating the Effectiveness of XAI Techniques in LLMs](https://mlsanity.com/paper/2503.05050/) (2025)
- [A Survey on fMRI-based Brain Decoding for Reconstructing Multimodal Stimuli](https://mlsanity.com/paper/2503.15978/) (2025)
- [Natural Language Generation in Healthcare: A Review of Methods and Applications](https://mlsanity.com/paper/2505.04073/) (2025)
- [Toxicity in Online Platforms and AI Systems: A Survey of Needs, Challenges, Mitigations, and Future Directions](https://mlsanity.com/paper/2509.25539/) (2025)
- [Optimizing Long-Form Clinical Text Generation with Claim-Based Rewards](https://mlsanity.com/paper/2510.02338/) (2025)
- [AIstorian lets AI be a historian: A KG-powered multi-agent system for accurate biography generation](https://mlsanity.com/paper/2503.11346/) (2025)
- [OrthoInsight: Rib Fracture Diagnosis and Report Generation Based on Multi-Modal Large Models](https://mlsanity.com/paper/2507.13993/) (2025)
- [MMD-Flagger: Leveraging Maximum Mean Discrepancy to Detect Hallucinations](https://mlsanity.com/paper/2506.01367/) (2025)
- [Progressive Code Integration for Abstractive Bug Report Summarization](https://mlsanity.com/paper/2512.00325/) (2025)
- [Responsible AI for General-Purpose Systems: Overview, Challenges, and A Path Forward](https://mlsanity.com/paper/2601.13122/) (2026)
- [Using Multimodal and Language-Agnostic Sentence Embeddings for Abstractive Summarization](https://mlsanity.com/paper/2603.08282/) (2026)
- [Evaluating Explainable AI Attribution Methods in Neural Machine Translation via Attention-Guided Knowledge Distillation](https://mlsanity.com/paper/2603.11342/) (2026)
- [A Training-Free Mixture-of-Agents Framework for Multi-Document Summarization using LLMs and Knowledge Graphs](https://mlsanity.com/paper/2606.03867/) (2026)
- [A Multi-Model Metric-based Selection Framework for Abstractive Text summarization](https://mlsanity.com/paper/2606.05494/) (2026)
- [Constrained Paraphrase Consistency for LLM Hallucination Detection](https://mlsanity.com/paper/2606.08158/) (2026)
- [Cluster-Aware Dual-Level Test Specification Generation for Large-Scale Automotive Software Requirements](https://mlsanity.com/paper/2606.17197/) (2026)
- [Dialogue Summarization with Emotion Dynamics Using Topic- and Participant-Centric Decomposition](https://mlsanity.com/paper/2607.14769/) (2026)

## Abstract (arXiv)

Specifically focusing on the landscape of abstractive text summarization, as opposed to extractive techniques, this survey presents a comprehensive overview, delving into state-of-the-art techniques, prevailing challenges, and prospective research directions. We categorize the techniques into traditional sequence-to-sequence models, pre-trained large language models, reinforcement learning, hierarchical methods, and multi-modal summarization. Unlike prior works that did not examine complexities, scalability and comparisons of techniques in detail, this review takes a comprehensive approach encompassing state-of-the-art methods, challenges, solutions, comparisons, limitations and charts out future improvements - providing researchers an extensive overview to advance abstractive summarization research. We provide vital comparison tables across techniques categorized - offering insights into model complexity, scalability and appropriate applications. The paper highlights challenges such as inadequate meaning representation, factual consistency, controllable text summarization, cross-lingual summarization, and evaluation metrics, among others. Solutions leveraging knowledge incorporation and other innovative strategies are proposed to address these challenges. The paper concludes by highlighting emerging research areas like factual inconsistency, domain-specific, cross-lingual, multilingual, and long-document summarization, as well as handling noisy data. Our objective is to provide researchers and practitioners with a structured overview of the domain, enabling them to better understand the current landscape and identify potential areas for further research and improvement.