Generating Short Product Descriptors Based on Very Little Training Data

We propose a pipeline model for summarising a short textual product description for inclusion in an online advertisement banner. While a standard approach is to truncate the advertiser’s original product description so that the text will fit the small banner, this simplistic approach often removes crucial information or attractive expressions from the original description. Our objective is to shorten the original description more intelligently, so that users’ click through rate (CTR) will improve. One major difficulty in this task, however, is the lack of large training data: machine learning methods that rely on thousands of pairs of the original and shortened texts would not be practical. Hence, our proposed method first employs a semisupervised sequence tagging method called TagLM to convert the original description into a sequence of entities, and then a BiLSTM entity ranker which determines which entities should be preserved: the main idea is to tackle the data sparsity problem by leveraging sequences of entities rather than sequences of words. In our offline experiments with Korean data from travel and fashion domains, our sequence tagger outperforms an LSTM-CRF baseline, and our entity ranker outperforms LambdaMART and RandomForest baselines. More importantly, in our online A/B testing where the proposed method was compared to the simple truncation approach, the CTR improved by 34.1% in the desktop PC environment.