  Through recent advancements in speech technologies and introduction of smart
assistants, such as Amazon Alexa, Apple Siri and Google Home, increasing number
of users are interacting with various applications through voice commands.
E-commerce companies typically display short product titles on their webpages,
either human-curated or algorithmically generated, when brevity is required.
However, these titles are dissimilar from natural spoken language. For example,
"Lucky Charms Gluten Free Break-fast Cereal, 20.5 oz a box Lucky Charms Gluten
Free" is acceptable to display on a webpage, while a similar title cannot be
used in a voice based text-to-speech application. In such conversational
systems, an easy to comprehend sentence, such as "a 20.5 ounce box of lucky
charms gluten free cereal" is preferred. Compared to display devices, where
images and detailed product information can be presented to users, short titles
for products which convey the most important information, are necessary when
interfacing with voice assistants. We propose eBERT, a sequence-to-sequence
approach by further pre-training the BERT embeddings on an e-commerce product
description corpus, and then fine-tuning the resulting model to generate short,
natural, spoken language titles from input web titles. Our extensive
experiments on a real-world industry dataset, as well as human evaluation of
model output, demonstrate that eBERT summarization outperforms comparable
baseline models. Owing to the efficacy of the model, a version of this model
has been deployed in real-world setting.
