# The Exploration of Knowledge-Preserving Prompts for Document Summarisation

arXiv: https://arxiv.org/abs/2301.11719 · HTML: https://mlsanity.com/paper/2301.11719/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 24 references that are in the map.
- First posted on arXiv 27 Jan 2023.

## Builds on (most cited first, 14 of 14)

- [Prefix-Tuning: Optimizing Continuous Prompts for Generation](https://mlsanity.com/paper/2101.00190/) (2021)
- [Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing](https://mlsanity.com/paper/2107.13586/) (2021)
- [Language Models as Knowledge Bases?](https://mlsanity.com/paper/1909.01066/) (2019)
- [Don't Give Me the Details, Just the Summary! Topic-Aware Convolutional Neural Networks for Extreme Summarization](https://mlsanity.com/paper/1808.08745/) (2018)
- [How Can We Know What Language Models Know?](https://mlsanity.com/paper/1911.12543/) (2019)
- [Abstractive Text Summarization Using Sequence-to-Sequence RNNs and Beyond](https://mlsanity.com/paper/1602.06023/) (2016)
- [GPT Understands, Too](https://mlsanity.com/paper/2103.10385/) (2021)
- [KG-BART: Knowledge Graph-Augmented BART for Generative Commonsense Reasoning](https://mlsanity.com/paper/2009.12677/) (2020)
- [Controllable Natural Language Generation with Contrastive Prefixes](https://mlsanity.com/paper/2202.13257/) (2022)
- [Factual Consistency Evaluation for Text Summarization via Counterfactual Estimation](https://mlsanity.com/paper/2108.13134/) (2021)
- [Control Prefixes for Parameter-Efficient Text Generation](https://mlsanity.com/paper/2110.08329/) (2021)
- [Tailor: A Prompt-Based Approach to Attribute-Based Controlled Text Generation](https://mlsanity.com/paper/2204.13362/) (2022)
- [OpenIE6: Iterative Grid Labeling and Coordination Analysis for Open Information Extraction](https://mlsanity.com/paper/2010.03147/) (2020)
- [Knowledge-aware Document Summarization: A Survey of Knowledge, Embedding Methods and Architectures](https://mlsanity.com/paper/2204.11190/) (2022)

## Built on by (most cited first, 1 of 1)

- [Enhancing Hindi NER in Low Context: A Comparative study of Transformer-based models with vs. without Retrieval Augmentation](https://mlsanity.com/paper/2507.16002/) (2025)

## Abstract (arXiv)

Despite the great development of document summarisation techniques nowadays, factual inconsistencies between the generated summaries and the original texts still occur from time to time. This study explores the possibility of adopting prompts to incorporate factual knowledge into generated summaries. We specifically study prefix-tuning that uses a set of trainable continuous prefix prompts together with discrete natural language prompts to aid summary generation. Experimental results demonstrate that the trainable prefixes can help the summarisation model extract information from discrete prompts precisely, thus generating knowledge-preserving summaries that are factually consistent with the discrete prompts. The ROUGE improvements of the generated summaries indicate that explicitly adding factual knowledge into the summarisation process could boost the overall performance, showing great potential for applying it to other natural language processing tasks.