# GSum: A General Framework for Guided Neural Abstractive Summarization

arXiv: https://arxiv.org/abs/2010.08014 · HTML: https://mlsanity.com/paper/2010.08014/

## Key facts

- Cited by 129 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Oct 2020.

## Built on by (most cited first, 30 of 129)

- [Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing](https://mlsanity.com/paper/2107.13586/) (2021)
- [BARTScore: Evaluating Generated Text as Text Generation](https://mlsanity.com/paper/2106.11520/) (2021)
- [BRIO: Bringing Order to Abstractive Summarization](https://mlsanity.com/paper/2203.16804/) (2022)
- [Revisiting the Gold Standard: Grounding Summarization Evaluation with Robust Human Evaluation](https://mlsanity.com/paper/2212.07981/) (2022)
- [SimCLS: A Simple Framework for Contrastive Learning of Abstractive Summarization](https://mlsanity.com/paper/2106.01890/) (2021)
- [Pretrained Language Models for Text Generation: A Survey](https://mlsanity.com/paper/2201.05273/) (2022)
- [BioBART: Pretraining and Evaluation of A Biomedical Generative Language Model](https://mlsanity.com/paper/2204.03905/) (2022)
- [Learning to Filter Context for Retrieval-Augmented Generation](https://mlsanity.com/paper/2311.08377/) (2023)
- [CPT: A Pre-Trained Unbalanced Transformer for Both Chinese Language Understanding and Generation](https://mlsanity.com/paper/2109.05729/) (2021)
- [Planning with Learned Entity Prompts for Abstractive Summarization](https://mlsanity.com/paper/2104.07606/) (2021)
- [An Empirical Survey on Long Document Summarization: Datasets, Models and Metrics](https://mlsanity.com/paper/2207.00939/) (2022)
- [Enabling Multimodal Generation on CLIP via Vision-Language Knowledge Distillation](https://mlsanity.com/paper/2203.06386/) (2022)
- [Benchmarking Generation and Evaluation Capabilities of Large Language Models for Instruction Controllable Summarization](https://mlsanity.com/paper/2311.09184/) (2023)
- [Can We Automate Scientific Reviewing?](https://mlsanity.com/paper/2102.00176/) (2021)
- [SummaReranker: A Multi-Task Mixture-of-Experts Re-ranking Framework for Abstractive Summarization](https://mlsanity.com/paper/2203.06569/) (2022)
- [Extractive Summarization via ChatGPT for Faithful Summary Generation](https://mlsanity.com/paper/2304.04193/) (2023)
- [From Sparse to Dense: GPT-4 Summarization with Chain of Density Prompting](https://mlsanity.com/paper/2309.04269/) (2023)
- [A Systematic Survey of Text Summarization: From Statistical Methods to Large Language Models](https://mlsanity.com/paper/2406.11289/) (2024)
- [Fighting Fire with Fire: The Dual Role of LLMs in Crafting and Detecting Elusive Disinformation](https://mlsanity.com/paper/2310.15515/) (2023)
- [Generative Knowledge Graph Construction: A Review](https://mlsanity.com/paper/2210.12714/) (2022)
- [Conditional Generation with a Question-Answering Blueprint](https://mlsanity.com/paper/2207.00397/) (2022)
- [Re-Examining System-Level Correlations of Automatic Summarization Evaluation Metrics](https://mlsanity.com/paper/2204.10216/) (2022)
- [ViT5: Pretrained Text-to-Text Transformer for Vietnamese Language Generation](https://mlsanity.com/paper/2205.06457/) (2022)
- [Controllable Neural Dialogue Summarization with Personal Named Entity Planning](https://mlsanity.com/paper/2109.13070/) (2021)
- [Sequence Level Contrastive Learning for Text Summarization](https://mlsanity.com/paper/2109.03481/) (2021)
- [ChatGPT-powered Conversational Drug Editing Using Retrieval and Domain Feedback](https://mlsanity.com/paper/2305.18090/) (2023)
- [A Well-Composed Text is Half Done! Composition Sampling for Diverse Conditional Generation](https://mlsanity.com/paper/2203.15108/) (2022)
- [Faithfulness in Natural Language Generation: A Systematic Survey of Analysis, Evaluation and Optimization Methods](https://mlsanity.com/paper/2203.05227/) (2022)
- [Towards Improving Faithfulness in Abstractive Summarization](https://mlsanity.com/paper/2210.01877/) (2022)
- [RefSum: Refactoring Neural Summarization](https://mlsanity.com/paper/2104.07210/) (2021)

## Abstract (arXiv)

Neural abstractive summarization models are flexible and can produce coherent summaries, but they are sometimes unfaithful and can be difficult to control. While previous studies attempt to provide different types of guidance to control the output and increase faithfulness, it is not clear how these strategies compare and contrast to each other. In this paper, we propose a general and extensible guided summarization framework (GSum) that can effectively take different kinds of external guidance as input, and we perform experiments across several different varieties. Experiments demonstrate that this model is effective, achieving state-of-the-art performance according to ROUGE on 4 popular summarization datasets when using highlighted sentences as guidance. In addition, we show that our guided model can generate more faithful summaries and demonstrate how different types of guidance generate qualitatively different summaries, lending a degree of controllability to the learned models.