# Attractive or Faithful? Popularity-Reinforced Learning for Inspired Headline Generation

arXiv: https://arxiv.org/abs/2002.02095 · HTML: https://mlsanity.com/paper/2002.02095/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Feb 2020.

## Built on by (most cited first, 12 of 12)

- [Faithfulness in Natural Language Generation: A Systematic Survey of Analysis, Evaluation and Optimization Methods](https://mlsanity.com/paper/2203.05227/) (2022)
- [Meta-Transfer Learning for Low-Resource Abstractive Summarization](https://mlsanity.com/paper/2102.09397/) (2021)
- [Harnessing the Power of LLMs: Evaluating Human-AI Text Co-Creation through the Lens of News Headline Generation](https://mlsanity.com/paper/2310.10706/) (2023)
- [XL-HeadTags: Leveraging Multimodal Retrieval Augmentation for the Multilingual Generation of News Headlines and Tags](https://mlsanity.com/paper/2406.03776/) (2024)
- [HonestBait: Forward References for Attractive but Faithful Headline Generation](https://mlsanity.com/paper/2306.14828/) (2023)
- [How to Generate Popular Post Headlines on Social Media?](https://mlsanity.com/paper/2309.09949/) (2023)
- [Let AI Entertain You: Increasing User Engagement with Generative AI and Rejection Sampling](https://mlsanity.com/paper/2312.12457/) (2023)
- [Generating Attractive and Authentic Copywriting from Customer Reviews](https://mlsanity.com/paper/2404.13906/) (2024)
- [SPEC: Summary Preference Decomposition for Low-Resource Abstractive Summarization](https://mlsanity.com/paper/2303.14011/) (2023)
- [COBART: Controlled, Optimized, Bidirectional and Auto-Regressive Transformer for Ad Headline Generation](https://mlsanity.com/paper/2607.08071/) (2026)
- [Towards Proactively Forecasting Sentence-Specific Information Popularity within Online News Documents](https://mlsanity.com/paper/2301.00152/) (2022)
- [Modeling Unified Semantic Discourse Structure for High-quality Headline Generation](https://mlsanity.com/paper/2403.15776/) (2024)

## Abstract (arXiv)

With the rapid proliferation of online media sources and published news, headlines have become increasingly important for attracting readers to news articles, since users may be overwhelmed with the massive information. In this paper, we generate inspired headlines that preserve the nature of news articles and catch the eye of the reader simultaneously. The task of inspired headline generation can be viewed as a specific form of Headline Generation (HG) task, with the emphasis on creating an attractive headline from a given news article. To generate inspired headlines, we propose a novel framework called POpularity-Reinforced Learning for inspired Headline Generation (PORL-HG). PORL-HG exploits the extractive-abstractive architecture with 1) Popular Topic Attention (PTA) for guiding the extractor to select the attractive sentence from the article and 2) a popularity predictor for guiding the abstractor to rewrite the attractive sentence. Moreover, since the sentence selection of the extractor is not differentiable, techniques of reinforcement learning (RL) are utilized to bridge the gap with rewards obtained from a popularity score predictor. Through quantitative and qualitative experiments, we show that the proposed PORL-HG significantly outperforms the state-of-the-art headline generation models in terms of attractiveness evaluated by both human (71.03%) and the predictor (at least 27.60%), while the faithfulness of PORL-HG is also comparable to the state-of-the-art generation model.