2020 COLING COLING 2020

On the Faithfulness for E-commerce Product Summarization

Abstract

AbstractIn 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.

🧭 Keyword Pioneer — product summarization
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Security & Privacy, Speech & Audio