On the Faithfulness for E -commerce Product Summarization (2020)

Abstract: In this work, we present a model to generate e-commerce product summaries. The consistency between the generated summary and the product attributes is an essential criterion for the ecommerce product summarization task. To enhance the consistency, first, we encode the product attribute table to guide the process of summary generation. Second, we identify the attribute words from the vocabulary, and we constrain these attribute words can be presented in the summaries only through copying from the source, i.e., the attribute words not in the source cannot be generated. We construct a Chinese e-commerce product summarization dataset, and the experimental results on this dataset demonstrate that our models significantly improve the faithfulness.

## introduction the fast growing of the e-commerce market makes information overload, which hinders users finding the products they need and slows e-commerce platform upgrading the marketing policies. product summarization technique facilitates user and e-commerce platform with text that contains the most valuable information about products, which is of practical value to address the problem of information overload. in the e-commerce scenarios, unfaithful product summaries that are inconsistent with the corresponding product attributes, e.g., generating "cotton" for a "silk dress", mislead the users and decrease public credibility of the e-commerce platform. thus, the faithfulness is a basic requirement for product summarization. recently, sequence-to-sequence (seq2seq) methods show promising performances for general text summarization tasks @xcite @xcite @xcite , which has been adopted to text generation tasks in the field of ecommerce @xcite @xcite @xcite . although applicable, they do not attempt to improve the faithfulness for product summarization. in this paper, we aim to produce faithful product summaries with heterogeneous data, i.e., textual product description and product attribute table, as shown in figure 1 . first, as the words in product attribute tables are explicit indicators for the product attributes, we propose a dual-copy mechanism that can selectively copy the tokens in textual product descriptions and product attribute words into the summaries. second, for the product attribute words, we only allow them appearing in the summaries through copying from the source. in this way, the attribute words not belonging to a certain product cannot be presented in the corresponding summary. thus, the generated summary will not contain incorrect attributes that contradict the product. our main contributions are as follows: ## dataset we collect the dataset from a mainstream chinese e-commerce platform. each sample is a (product textual description, product attribute table, product summary) triplet. the product summaries are generated by thousands of qualified experts, and the auditing groups of the e-commerce platform strictly verify the quality. we collect 361,158 and 92,886 samples for the categories of home appliances and bags, respectively. for home appliances, we randomly select 10,000 samples as the validation set and test set. for bags category, we randomly select 5,000 samples as the validation set and test set. the remaining samples are used as the training set. the average number of the chinese characters for the source text and the summary are 325.54 and 79.24, respectively. the average count of product attribute is 13.87. ## conclusion we present an e-commerce product summarization model that aims to improve the consistency between the generated summary and the product attributes. we propose two strategies. first, we introduce a dual-copy mechanism that can selectively copy words from both the textual product descriptions and the product attribute table, which makes our model inclined to produce faithful attribute words existing in the product attribute table. second, we design a heuristic method to recognize the attribute words, and unfaithful attribute words are not allowed to be presented in the summaries through generating from the target vocabulary. we construct a large-scale chinese e-commerce product summarization dataset, and our dataset and code are available 1 .