On the Faithfulness for E-commerce Product Summarization

Proceedings of the 28th International Conference on Computational Linguistics, pages 5712–5717 Barcelona, Spain (Online), December 8-13, 2020 5712 On the Faithfulness for E-commerce Product Summarization Peng Yuan, Haoran Li∗, Song Xu, Youzheng Wu, Xiaodong He and Bowen Zhou JD AI Research {yuanpeng29,lihaoran24,xusong28}@jd.com {wuyouzheng1,xiaodong.he,bowen.zhou}@jd.com 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 e- commerce 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 sum- maries 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 experi- mental results on this dataset demonstrate that our models significantly improve the faithfulness. 1 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 sum- marization 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 correspond- ing 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 sum- marization. Recently, sequence-to-sequence (seq2seq) methods show promising performances for general text summarization tasks (Rush et al., 2015; Chopra et al., 2016; Zhou et al., 2017; Li et al., 2018; Zhang et al., 2018; Li et al., 2020b), which has been adopted to text generation tasks in the field of e- commerce (Khatri et al., 2018; Yang et al., 2018; Daultani et al., 2019; Chen et al., 2019; Li et al., 2020a). 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: • We propose a e-commerce product summarizer that can copy the tokens both from textual product descriptions and the product attribute table. • We design an attribute-word-aware decoder that guarantees the attribute words can be …

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