The ubiquitous nature of the Internet and its applicability as an inexpensive advertising media has resulted in Web and mobile platforms to be on target for product advertising. Existing advertising approaches on different social media platforms include sponsored search, e-mail advertising, banner ads, etc., to attract customers. The current systems for advertising on these platforms require the advertisers to manually come up with a catchy tagline, which is time-consuming and a challenging task. To overcome this problem, we propose a novel framework to automatically generate novel and catchy taglines tailored for advertisers' products. The framework consists of a domain-specific knowledge graph, a novel modified long short-term memory (LSTM) (we call it CopyLSTM), and a neural paraphrase model. We also curate and build a database of fashion products with 100K input product features and 300K sentences (three sentences per product data). This dataset is used to populate the knowledge graph and learn the modified LSTM to generate taglines. The proposed framework is evaluated on different metrics for sentence quality and compared with the state-of-art methods for tagline generation. Subjective evaluation was performed to understand the novelty and creativity of the generated tagline.