Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Product Title Generation for Conversational Systems using BERT   Through recent advancements in speech technology and introduction of smart
devices, such as Amazon Alexa and Google Home, increasing number of users are
interacting with applications through voice. E-commerce companies typically
display short product titles on their webpages, either human-curated or
algorithmically generated, when brevity is required, but 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, but "a 20.5 ounce box of lucky charms gluten free cereal"
is easier to comprehend over a conversational system. As compared to display
devices, where images and detailed product information can be presented to
users, short titles for products are necessary when interfacing with voice
assistants. We propose a sequence-to-sequence approach using BERT to generate
short, natural, spoken language titles from input web titles. Our extensive
experiments on a real-world industry dataset and human evaluation of model
outputs, demonstrate that BERT summarization outperforms comparable baseline
models.

28
The categories of this paper are: alg-geom
5
The categories of this paper are: gr-qc
20
The categories of this paper are: acc-phys
4
The categories of this paper are: cond-mat
29
The categories of this paper are: funct-an
18
The categories of this paper are: econ
3
The categories of this paper are: physics
2
The categories of this paper are: math
7
The categories of this paper are: hep-th
26
The categories of this paper are: bayes-an
32
The categories of this paper are: atom-ph
0
The categories of this paper are: hep-ph
25
The categories of this paper are: patt-sol
37
The categories of this paper are: supr-con
22
The categories of this paper are: chao-dyn
31
The categories of this paper are: ao-sci