Distinctive Slogan Generation with Reconstruction (2020)

Abstract: E-commerce sites include advertising slogans along with information regarding an item. Slogans can attract viewers’ attention to increase sales or visits by emphasizing advantages of an item. The aim of this study is to generate a slogan from a description of an item. To generate a slogan, we apply an encoder–decoder model which has shown effectiveness in many kinds of natural language generation tasks, such as abstractive summarization. However, slogan generation task has three characteristics that distinguish it from other natural language generation tasks: distinctiveness, topic emphasis, and style difference. To handle these three characteristics, we propose a compressed representation–based reconstruction model with refer–attention and conversion layers. The results of the experiments indicate that, based on automatic and human evaluation, our method achieves higher performance than conventional methods.

## introduction advertisements, e-commerce sites, and flyers include advertising slogans along with item information, such as descriptions, prices, features, and pictures. although the purpose of item information is to tell accurate and detailed information, the aim of slogans is to attract viewers' attention to increase sales or visits. therefore, a slogan often emphasizes a part of advantages that an item holds, and it often contains peculiar words or expression to draw viewers' attention. creating slogans is cumbersome and costly. we propose a method to generate a slogan from a description of a target item to support the writers of slogans. figure 1 shows an example of a description and a slogan for a job matching. a slogan is similar to an abstractive summary to some extent; a target sentence is shorter than a description and includes words not presented in the description. however, to attract viewers, slogans should have three unique characteristics. distinctiveness: a slogan should be distinctive towards the target item. when a slogan is generic and suitable also for any other items, it does not increase the viewer's motivation to select the target item. therefore, a slogan should describe the detail of a target-item. for example, "don't go with the flow, but make a new flow by developing business social media" is better than "why don't you apply to our company? we need you right now!" because the former provides more information regarding the job. topic emphasis: a slogan should examine a specific topic in the description because it is difficult to contain all aspects within a short tip. in fact, the example slogan in figure 1 does not include phrases such as "business social media," which is one of the main topics in the description. style difference: a slogan should be written in an impressive writing style to attract viewers' interests. therefore, a slogan differs from a description in terms of style and vocabulary. for instance, the first sentence of the example slogan is not just an explanation but an interrogative sentence. to generate a slogan, we apply an encoder-decoder model @xcite since encoderdecoder models have shown effectiveness in abstractive summarization, and slogan generation shares some characteristics with abstractive summarization. however, slogan generation has three characteristics that differentiate it from abstractive summarization: distinctiveness, topic emphasis, and style difference. in particular, summarization models lose distinctiveness because their outputs tend to be generic @xcite . ## related work slogan generation: slogan generation has been addressed through non-neural approaches. one approach is to extract and revise a sentence from a corpus @xcite . another approach is to construct skeletons of slogans and fill arbitrary words into the skeletons @xcite . in one study, suggestions for rephrasing written slogans were provided to assist slogan writers @xcite . some approaches use a description of a target item @xcite , similarly to our method. these approaches consist from three steps: selecting slogans from a corpus as candidates, rephrasing candidate slogans, and sorting these slogans. however, the generated slogans are not creative because these methods essentially rephrase existing slogans. in contrast, our method is a neural-based generation model that generates slogans that are different from those in a corpus. the comparison of creativity between our method and a non-neural method is described in section 5.3, in addition to the generation performance. abstractive summarization: summarization has been tackled by extractive and rule-based approaches @xcite @xcite . however, neural based abstractive approaches have become very common and achieved high performance because of reconstruction model (crr) @xcite ## distinctive slogan generation with reconstruction a slogan should reflect a distinctive and important part of an item description. the more distinctive a slogan is, the easier the reconstruction of the corresponding description from the slogan is. this is because such slogans facilitate the estimation of the description. for example, the estimation of the corresponding description from the slogan "we need new staffs!" is difficult because it can fit to any other job. to enhance the distinctiveness of generated slogans, we define and minimize a reconstruction loss as an indicator of distinctiveness by introducing a compressed representation-based reconstruction model. to encourage topic emphasis and style difference, refer-attention and conversion layers are used, respectively. figure 2 shows the outline of our method. ## conclusion the aim of this study is to generate a slogan from the description of a target item. we firstly apply an encoder-decoder model to the slogan generation task. then, we focus on the three characteristics of slogan generation: distinctiveness, topic emphasis, style difference. to enhance the distinctiveness of generated slogans, we define and minimize a reconstruction loss as an indicator of distinctiveness by introducing a compressed representation-based reconstruction model. to encourage topic emphasis and style difference, refer-attention and conversion layers are used, respectively. automatic and human evaluation demonstrated that our method is superior to the conventional methods. in addition, we observed that neural methods are superior to a non-neural method in terms of rouge-l performance and creativity. in future work, we are planning to make a generation model which can directly enhance sales or visits by optimizing more straightforward e-commerce signals. moreover, we will like to conduct an experiment with another dataset to ensure the generalizability of our method.