# Probing Product Description Generation via Posterior Distillation arXiv: https://arxiv.org/abs/2103.01594 ยท HTML: https://mlsanity.com/paper/2103.01594/ ## Key facts - Cited by 15 papers in the map (data through 14 Oct 2026). - Builds on 24 of its 41 references that are in the map. - First posted on arXiv 2 Mar 2021. ## Builds on (most cited first, 24 of 24) - [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017) - [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019) - [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015) - [Gaussian Error Linear Units (GELUs)](https://mlsanity.com/paper/1606.08415/) (2016) - [Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation](https://mlsanity.com/paper/1609.08144/) (2016) - [Get To The Point: Summarization with Pointer-Generator Networks](https://mlsanity.com/paper/1704.04368/) (2017) - [Multi-News: a Large-Scale Multi-Document Summarization Dataset and Abstractive Hierarchical Model](https://mlsanity.com/paper/1906.01749/) (2019) - [Hierarchical Transformers for Multi-Document Summarization](https://mlsanity.com/paper/1905.13164/) (2019) - [POG: Personalized Outfit Generation for Fashion Recommendation at Alibaba iFashion](https://mlsanity.com/paper/1905.01866/) (2019) - [The JDDC Corpus: A Large-Scale Multi-Turn Chinese Dialogue Dataset for E-commerce Customer Service](https://mlsanity.com/paper/1911.09969/) (2019) - [CDL: Curriculum Dual Learning for Emotion-Controllable Response Generation](https://mlsanity.com/paper/2005.00329/) (2020) - [Towards Knowledge-Based Personalized Product Description Generation in E-commerce](https://mlsanity.com/paper/1903.12457/) (2019) - [Modeling Semantic Relationship in Multi-turn Conversations with Hierarchical Latent Variables](https://mlsanity.com/paper/1906.07429/) (2019) - [Regularizing Dialogue Generation by Imitating Implicit Scenarios](https://mlsanity.com/paper/2010.01893/) (2020) - [User-Inspired Posterior Network for Recommendation Reason Generation](https://mlsanity.com/paper/2102.07919/) (2021) - [Learning to Select Context in a Hierarchical and Global Perspective for Open-domain Dialogue Generation](https://mlsanity.com/paper/2102.09282/) (2021) - [Multi-Modal Generative Adversarial Network for Short Product Title Generation in Mobile E-Commerce](https://mlsanity.com/paper/1904.01735/) (2019) - [Multi-Source Pointer Network for Product Title Summarization](https://mlsanity.com/paper/1808.06885/) (2018) - [Automatic Generation of Chinese Short Product Titles for Mobile Display](https://mlsanity.com/paper/1803.11359/) (2018) - [Compose Like Humans: Jointly Improving the Coherence and Novelty for Modern Chinese Poetry Generation](https://mlsanity.com/paper/2005.01556/) (2020) - [Two Birds, One Stone: A Simple, Unified Model for Text Generation from Structured and Unstructured Data](https://mlsanity.com/paper/1909.10158/) (2019) - [Personalized Emphasis Framing for Persuasive Message Generation](https://mlsanity.com/paper/1607.08898/) (2016) - [A Multi-task Learning Approach for Improving Product Title Compression with User Search Log Data](https://mlsanity.com/paper/1801.01725/) (2018) - [Speeding Up Neural Machine Translation Decoding by Cube Pruning](https://mlsanity.com/paper/1809.02992/) (2018) ## Built on by (most cited first, 15 of 15) - [Automatic Product Copywriting for E-Commerce](https://mlsanity.com/paper/2112.11915/) (2021) - [Intelligent Online Selling Point Extraction for E-Commerce Recommendation](https://mlsanity.com/paper/2112.10613/) (2021) - [A Multimodal In-Context Tuning Approach for E-Commerce Product Description Generation](https://mlsanity.com/paper/2402.13587/) (2024) - [Multimodal Prompt Learning for Product Title Generation with Extremely Limited Labels](https://mlsanity.com/paper/2307.01969/) (2023) - [SINGA-Easy: An Easy-to-Use Framework for MultiModal Analysis](https://mlsanity.com/paper/2108.02572/) (2021) - [VisualTextRank: Unsupervised Graph-based Content Extraction for Automating Ad Text to Image Search](https://mlsanity.com/paper/2108.02725/) (2021) - [Text is NOT Enough: Integrating Visual Impressions into Open-domain Dialogue Generation](https://mlsanity.com/paper/2109.05778/) (2021) - [Discovering Personalized Semantics for Soft Attributes in Recommender Systems using Concept Activation Vectors](https://mlsanity.com/paper/2202.02830/) (2022) - [Reinforced MOOCs Concept Recommendation in Heterogeneous Information Networks](https://mlsanity.com/paper/2203.11011/) (2022) - [Explanation-Guided Fairness Testing through Genetic Algorithm](https://mlsanity.com/paper/2205.08335/) (2022) - [Using Graph Neural Networks for Program Termination](https://mlsanity.com/paper/2207.14648/) (2022) - [Draft, Command, and Edit: Controllable Text Editing in E-Commerce](https://mlsanity.com/paper/2208.05623/) (2022) - [Tribe or Not? Critical Inspection of Group Differences Using TribalGram](https://mlsanity.com/paper/2303.09664/) (2023) - [Towards Zero-Shot Personalized Table-to-Text Generation with Contrastive Persona Distillation](https://mlsanity.com/paper/2304.08911/) (2023) - [Modeling Comparative Logical Relation with Contrastive Learning for Text Generation](https://mlsanity.com/paper/2406.09095/) (2024) ## Abstract (arXiv) In product description generation (PDG), the user-cared aspect is critical for the recommendation system, which can not only improve user's experiences but also obtain more clicks. High-quality customer reviews can be considered as an ideal source to mine user-cared aspects. However, in reality, a large number of new products (known as long-tailed commodities) cannot gather sufficient amount of customer reviews, which brings a big challenge in the product description generation task. Existing works tend to generate the product description solely based on item information, i.e., product attributes or title words, which leads to tedious contents and cannot attract customers effectively. To tackle this problem, we propose an adaptive posterior network based on Transformer architecture that can utilize user-cared information from customer reviews. Specifically, we first extend the self-attentive Transformer encoder to encode product titles and attributes. Then, we apply an adaptive posterior distillation module to utilize useful review information, which integrates user-cared aspects to the generation process. Finally, we apply a Transformer-based decoding phase with copy mechanism to automatically generate the product description. Besides, we also collect a large-scare Chinese product description dataset to support our work and further research in this field. Experimental results show that our model is superior to traditional generative models in both automatic indicators and human evaluation.