Selection and Generation: Learning towards Multi-Product Advertisement Post Generation (2020)

Abstract: As the E-commerce thrives, high-quality online advertising copywriting has attracted more and more attention. Different from the advertising copywriting for a single product, an advertisement (AD) post includes an attractive topic that meets the customer needs and description copywriting about several products under its topic. A good AD post can highlight the characteristics of each product, thus helps customers make a good choice among candidate products. Hence, multi-product AD post generation is meaningful and important. We propose a novel end-to-end model named S-MG Net to generate the AD post. Targeted at such a challenging real-world problem, we split the AD post generation task into two subprocesses: (1) select a set of products via the SelectNet (Selection Network). (2) generate a post including selected products via the MGenNet (Multi-Generator Network). Concretely, SelectNet first captures the post topic and the relationship among the products to output the representative products. Then, MGenNet generates the description copywriting of each product. Experiments conducted on a large-scale real-world AD post dataset demonstrate that our proposed model achieves impressive performance in terms of both automatic metrics as well as human evaluations.

## introduction with the fantastic development of the internet, ecommerce has a rapid development and changes the customary shopping way of most people in the world. advertising copywriting is a huge factor in e-commerce, and well-written advertising copywriting can encourage consumers to understand further and purchase products. however, there is an important restriction factor for traditional advertising copywriting, i.e., the writing efficiency of human copywriters cannot match the growth rate of new products. many e-commerce websites, such as amazon foot_0 , taobao foot_1 and jd foot_2 , have billions of products, so it is impossible to write all copywriting manually. to address this issue, researchers pay more and more attention to the automatic advertising copywriting generation. the initial work @xcite on automatic advertising copywriting generation is based on the predefined template. with the surge of deep learning techniques, the neural encoder-decoder framework shows remarkable effects on various text generation tasks, e.g., sum-marization @xcite @xcite , story generation @xcite @xcite and so on. the researchers from academia and industry begin to explore how to generate product advertising copywriting through deep learning methods. @xcite propose a pointer-generator model to generate the product advertising copywriting whose patterns are controlled. @xcite explore a new way to generate personalized product copywriting by enhancing the transformer with an extra knowledge base. however, all the previous studies focus on copywriting generation for a single product. in such case, consumers need to compare among products and summarize the advantages and disadvantages of each product by themselves. this makes online shopping a challenging and time-consuming task for customers with high requirements for products. consequently, a novel advertising copywriting format called multi-product ad post is becoming increasingly popular these years. a multi-product ad post contains several related products that either function similarly or match each other. each product in the post has its own copywriting, and every copywriting will take full account of the topic of the post and other products' information. the characteristic information of each product can be highlighted by considering both the post topic and other products' information, so the post can let customers understand the characteristics of each product quickly. we show a case of ad post in figure 1 and we can find the keyword "fat" in the post title and it's the reason for the first product copywriting to emphasize "high-calorie" attribute of the products. meanwhile, besides the most basic information about cereal, copywriting about these two products emphasize the unique features, such as "rose" for the first product and "yogurt" for the second. however, the post writing is far more difficult than single product copywriting. first, it needs to select suitable products for writing in a post. then the copywriter must consider the relationship between the post topic and the products to describe each product clearly. meanwhile, to describe the unique characteristics of each product, the copywriter should also consider the information of other products in the post. therefore, the multi-product ad post can only be used on a few hot products yet because of the low writing speed and the high cost. thus, it is very necessary to realize the automatic generation of multi-product ad post. targeted to automatic multi-product ad post generation, we propose a two-stage model named s-mg net. in this model, we split the multi-product ad post generation task into two subprocesses: (1) select a set of products via the selectnet (selection network). (2) generate a post including selected products via the mgennet (multi-generator network). concretely, we first propose an iterative attention mechanism called select-attention (se-lectattn) and build a model called selectnet. selec-tattn allows the selectnet to select the appropriate combination of the products based on the post topic and the relationship among the products. then, we design the mgennet inspired by the multi-agent communication method to generate each product's copywriting considering the topic and other products. in detail, we let each agent, i.e., the generator from mgennet, generate product copywriting individually. meanwhile, we propose an agent communication strategy that can let each agent obtain the information of other products when the product copywriting is generating. finally, we combine all the generated copywriting to form the whole multi-product ad post. in a nutshell, our contributions can be summarized as: (1) we propose the selectnet which can select the appropriate combination of the products based on the post topic and the relationship among the products. (2) we build the mgennet to generate the copywriting of each product based on the multi-agent communication framework. (3) we combine the above networks as an end-to-end s-mg net which can generate attractive ad posts. ## related work text generation. text generation has become one of the hottest subfields of natural language processing. previous researches mainly focus on several popular text generation tasks, such as dialogue generation @xcite @xcite and story generation @xcite @xcite . @xcite are proposed to improve wording novelty. @xcite target to the intra-sentence consistency and thematic consistency is improved by @xcite ; @xcite . besides, text generation from different data formats have also been widely studied in recent years, e.g., table-to-text generation @xcite , which can fit various data formats in real-world scenarios. multi-agent communication. commnet proposed by @xcite is the first deep learning framework for multi-agent communication. there are several previous works built on the commnet framework, for example, researchers use the multi-agent communication method to play the starcraft games @xcite . mordatch and abbeel (2018) deal with natural language processing tasks such as machine translation and sentiment analysis with the multi-agent communication method. @xcite present the first study using the multi-agent framework for summarization. product descriptions generation. product description copywriting is critical for the e-commerce platform, and automatically generating the product description copywriting has attracted considerable interest from both academia and industry because of its importance. wang et al. (2017 ) first focus on the product description generation task and incorporates the preset template to generate product descriptions automatically. with the development of deep learning, zhang et al. (2019) proposed a pointer-based generation model with a dual encoder to generate product description and achieved the controlled patterns. chen et al. (2019a) proposed a transformer-based model to generate personalized product descriptions by combining a knowledge graph as the extra knowledge base. chan et al. (2019a) proposed use the entity label to enhance the fidelity of the product description. ## problem formulation to formulate the multi-product ad post generation task, we use p to denote a product candidate set which contains a lot of productions, namely @xmath0, u p 2 , • • • , u p np }, where u p i indicates the information 4 of i-th product in the set and n p is the set size. specifically, u p @xmath1,1 , w p i,2 , • • • , w p i,n p,i } is a text sequence which contains n p,i words. meanwhile, the topic of the ad post is represented as @xmath2, w t 2 , • • • , w t nt } where w t i indicates the i-th word in the topic sequence and n t is the length. the goal of our model is to generate a multi-product ad post ĉ = {û c 1 , ûc 2 , • • • , ûc nc } where ûc ## proposed model as figure 3 shows, our proposed s-mg net model consists of selection network (selectnet) and multi-generator network (mgennet). ## conclusion and future work in this paper, we explore the multi-product ad post generation which is meaningful for both academic researches and industrial applications. due to the characteristics of such a new task, most existing classical text generation schemes do not work well. to fill this gap, we propose an end-to-end s-mg net model that uses the selectnet to select the combination of products associated with each other and uses the mgennet to generate the copywriting for each product. after stitching the generated copywriting together, we can obtain a well-formed multi-product ad post. experiments conducted a large-scale real-world product description dataset demonstrate that our proposed model achieves promising and impressive performance. owing to the promising results of the proposed two-stage model, we could degenerate this process into more specific sub-processes. moreover, we will conduct further exploration of the multiproduct ad post form, including more vivid multimedia information, such as pictures and videos.