Hooks in the Headline: Learning to Generate Headlines with Controlled Styles (2004.01980v3)

Abstract: Current summarization systems only produce plain, factual headlines, but do not meet the practical needs of creating memorable titles to increase exposure. We propose a new task, Stylistic Headline Generation (SHG), to enrich the headlines with three style options (humor, romance and clickbait), in order to attract more readers. With no style-specific article-headline pair (only a standard headline summarization dataset and mono-style corpora), our method TitleStylist generates style-specific headlines by combining the summarization and reconstruction tasks into a multitasking framework. We also introduced a novel parameter sharing scheme to further disentangle the style from the text. Through both automatic and human evaluation, we demonstrate that TitleStylist can generate relevant, fluent headlines with three target styles: humor, romance, and clickbait. The attraction score of our model generated headlines surpasses that of the state-of-the-art summarization model by 9.68%, and even outperforms human-written references.

• The paper introduces Stylistic Headline Generation (SHG), a new task for generating headlines with controlled styles, proposing TitleStylist as a novel model.
• TitleStylist uses a multitask learning framework, parameter sharing, and style-dependent layer normalization to disentangle content and style for effective transfer.
• Evaluation shows TitleStylist generates style-rich headlines, achieving 9.68% higher attraction scores and outperforming state-of-the-art models without losing relevance.

Stylistic Headline Generation: Exploring TitleStylist

The paper "Hooks in the Headline: Learning to Generate Headlines with Controlled Styles" addresses the limitations of traditional headline generation systems, which typically produce only factual headlines without stylistic flair. The authors introduce a new task named Stylistic Headline Generation (SHG), which allows generation of headlines with targeted stylistic elements such as humor, romance, and clickbait. This approach is motivated by the practical need for more engaging and memorable headlines that capture readers' attention in a competitive digital media landscape.

Proposed Methodology

The authors propose a novel model, TitleStylist, which leverages a multitask learning framework integrating both summarization and reconstruction tasks. The model is designed without style-specific pairs of article-headline data, relying instead on a standard dataset for headline summarization and mono-style corpora. TitleStylist employs a parameter-sharing approach to disentangle content from style, utilizing style-dependent layer normalization and guided encoder-attention within the transformer architecture. This allows for effective style transfer while maintaining the semantic relevance of headlines to the provided text.

Evaluation and Results

The evaluative process encompasses both automatic and human judgments, indicating that TitleStylist excels in generating headlines with specified styles. The quantitative assessments indicate that headlines produced by TitleStylist achieved a 9.68% higher attraction score than those generated by state-of-the-art models, even outperforming human-written references. Human evaluators assess the model’s output based on relevance, attraction, fluency, and style strength. TitleStylist generates stylistically rich headlines without significantly compromising relevance, as evidenced by its competitive performance in both human and automatic evaluations.

Implications and Future Directions

This research contributes a significant tool for automated content generation, especially crucial in advertising, journalism, and social media, where reader engagement is key. The advanced capability of generating diverse and style-specific headlines presents immediate applications for companies striving for compelling media presence. The underlying methodology also adds value to the broader domain of text style transfer, showcasing the potential to apply similar frameworks to various text generation tasks.

Future research queries may involve expanding the application scope of SHG by incorporating more nuanced and complex styles. Exploration into even more flexible models, which adapt to dynamically evolving content tones in real-time scenarios, can also be a prospective avenue. Additionally, consideration of ethical implications and biases associated with automated headline generation is essential to ensure responsible AI utilization.

In conclusion, the paper advances the state-of-the-art in headline generation by marrying stylistic attributes effectively with content summarization, facilitating a higher degree of user engagement through creative headlines.