Hooks in the Headline: Learning to Generate Headlines with Controlled Styles (2020)

Abstract: Current summarization systems only produce plain, factual headlines, far from the practical needs for the exposure and memorableness of the articles. We propose a new task, Stylistic Headline Generation (SHG), to enrich the headlines with three style options (humor, romance and clickbait), thus attracting more readers. With no style-specific article-headline pair (only a standard headline summarization dataset and mono-style corpora), our method TitleStylist generates stylistic 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 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 outperforms the state-of-the-art summarization model by 9.68%, even outperforming human-written references.

## introduction every good article needs a good title, which should not only be able to condense the core meaning of the text, but also sound appealing to the readers for more exposure and memorableness. however, currently even the best headline generation (hg) system can only fulfill the above requirement yet performs poorly on the latter. for example, in figure 1 , the plain headline by an hg model "summ: leopard frog found in new york city" is less eye-catching than the style-carrying ones such as "what's that chuckle you hear? it may be the new frog from nyc." to bridge the gap between the practical needs for attractive headlines and the plain hg by the current summarization systems, we propose a new task of stylistic headline generation (shg). given an article, it aims to generate a headline with a target style such as humorous, romantic, and click-baity. it has broad applications in reader-adapted title generation, slogan suggestion, auto-fill for online post headlines, and many others. shg is a highly skilled creative process, and usually only possessed by expert writers. one of the most famous headlines in american publications, "sticks nix hick pix," could be such an example. in contrast, the current best summarization systems are at most comparable to novice writers who provide a plain descriptive representation of the text body as the title @xcite @xcite . these systems usually use a language generation model that mixes styles with other linguistic patterns and inherently lacks a mechanism to control the style explicitly. more fundamentally, the training data comprise of a mixture of styles (e.g., the gigaword dataset @xcite ), obstructing the models from learning a distinct style. in this paper, we propose the new task shg, to emphasize the explicit control of style in headline generation. we present a novel headline generation model, titlestylist, to produce enticing titles with target styles including humorous, romantic, and click-baity. our model leverages a multitasking framework to train both a summarization model on headline-article pairs, and a denoising autoencoder (dae) on a style corpus. in particular, based on the transformer architecture @xcite , we use the style-dependent layer normalization and the style-guided encoder-attention to disentangle the language style factors from the text. this design enables us to use the shared content to generate headlines that are more relevant to the articles, as well as to control the style by plugging in a set of style-specific parameters. we validate the model on three tasks: humorous, romantic, and click-baity headline generation. both automatic and human evaluations show that titlestylist can generate headlines with the desired styles that appeal more to human readers, as in figure 1 . the main contributions of our paper are listed below: ## related work our work is related to summarization and text style transfer.