# Topic-to-essay generation with knowledge-based content selection

arXiv: https://arxiv.org/abs/2402.16248 · HTML: https://mlsanity.com/paper/2402.16248/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 19 references that are in the map.
- First posted on arXiv 26 Feb 2024.

## Builds on (most cited first, 8 of 8)

- [Prefix-Tuning: Optimizing Continuous Prompts for Generation](https://mlsanity.com/paper/2101.00190/) (2021)
- [A Diversity-Promoting Objective Function for Neural Conversation Models](https://mlsanity.com/paper/1510.03055/) (2015)
- [ConceptNet 5.5: An Open Multilingual Graph of General Knowledge](https://mlsanity.com/paper/1612.03975/) (2016)
- [Editable Neural Networks](https://mlsanity.com/paper/2004.00345/) (2020)
- [MDERank: A Masked Document Embedding Rank Approach for Unsupervised Keyphrase Extraction](https://mlsanity.com/paper/2110.06651/) (2021)
- [GENIUS: Sketch-based Language Model Pre-training via Extreme and Selective Masking for Text Generation and Augmentation](https://mlsanity.com/paper/2211.10330/) (2022)
- [A Sentiment-Controllable Topic-to-Essay Generator with Topic Knowledge Graph](https://mlsanity.com/paper/2010.05511/) (2020)
- [Topic-to-Essay Generation with Comprehensive Knowledge Enhancement](https://mlsanity.com/paper/2106.15142/) (2021)

## Abstract (arXiv)

The topic-to-essay generation task is a challenging natural language generation task that aims to generate paragraph-level text with high semantic coherence based on a given set of topic words. Previous work has focused on the introduction of external knowledge, ignoring the insufficient generated text diversity. In order to improve the generation diversity, we propose a novel copy mechanism model with a content selection module that integrates rich semantic knowledge from the language model into the decoder. Furthermore, we introduce the improved prefix tuning method to train the model, enabling it to adapt to varying input complexities. In addition, we have contributed a new Chinese dataset for TEG tasks. Experimental results demonstrate that the proposed model can improve the generated text diversity by 35% to 59% compared to the state-of-the-art method, while maintaining a high level of topic consistency.