# The Style-Content Duality of Attractiveness: Learning to Write Eye-Catching Headlines via Disentanglement

arXiv: https://arxiv.org/abs/2012.07419 · HTML: https://mlsanity.com/paper/2012.07419/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Dec 2020.

## Built on by (most cited first, 7 of 7)

- [ACI-BENCH: a Novel Ambient Clinical Intelligence Dataset for Benchmarking Automatic Visit Note Generation](https://mlsanity.com/paper/2306.02022/) (2023)
- [Keywords and Instances: A Hierarchical Contrastive Learning Framework Unifying Hybrid Granularities for Text Generation](https://mlsanity.com/paper/2205.13346/) (2022)
- [Follow the Timeline! Generating Abstractive and Extractive Timeline Summary in Chronological Order](https://mlsanity.com/paper/2301.00867/) (2023)
- [EZInterviewer: To Improve Job Interview Performance with Mock Interview Generator](https://mlsanity.com/paper/2301.00972/) (2023)
- [Exploring Cross-lingual Textual Style Transfer with Large Multilingual Language Models](https://mlsanity.com/paper/2206.02252/) (2022)
- [Gradient-guided Unsupervised Text Style Transfer via Contrastive Learning](https://mlsanity.com/paper/2202.00469/) (2022)
- [A Survey of Text Style Transfer: Applications and Ethical Implications](https://mlsanity.com/paper/2407.16737/) (2024)

## Abstract (arXiv)

Eye-catching headlines function as the first device to trigger more clicks, bringing reciprocal effect between producers and viewers. Producers can obtain more traffic and profits, and readers can have access to outstanding articles. When generating attractive headlines, it is important to not only capture the attractive content but also follow an eye-catching written style. In this paper, we propose a Disentanglement-based Attractive Headline Generator (DAHG) that generates headline which captures the attractive content following the attractive style. Concretely, we first devise a disentanglement module to divide the style and content of an attractive prototype headline into latent spaces, with two auxiliary constraints to ensure the two spaces are indeed disentangled. The latent content information is then used to further polish the document representation and help capture the salient part. Finally, the generator takes the polished document as input to generate headline under the guidance of the attractive style. Extensive experiments on the public Kuaibao dataset show that DAHG achieves state-of-the-art performance. Human evaluation also demonstrates that DAHG triggers 22% more clicks than existing models.