  Product attribute extraction is an growing field in e-commerce business, with
several applications including product ranking, product recommendation, future
assortment planning and improving online shopping customer experiences.
Understanding the customer needs is critical part of online business,
specifically fashion products. Retailers uses assortment planning to determine
the mix of products to offer in each store and channel, stay responsive to
market dynamics and to manage inventory and catalogs. The goal is to offer the
right styles, in the right sizes and colors, through the right channels. When
shoppers find products that meet their needs and desires, they are more likely
to return for future purchases, fostering customer loyalty. Product attributes
are a key factor in assortment planning. In this paper we present PAE, a
product attribute extraction algorithm for future trend reports consisting text
and images in PDF format. Most existing methods focus on attribute extraction
from titles or product descriptions or utilize visual information from existing
product images. Compared to the prior works, our work focuses on attribute
extraction from PDF files where upcoming fashion trends are explained. This
work proposes a more comprehensive framework that fully utilizes the different
modalities for attribute extraction and help retailers to plan the assortment
in advance. Our contributions are three-fold: (a) We develop PAE, an efficient
framework to extract attributes from unstructured data (text and images); (b)
We provide catalog matching methodology based on BERT representations to
discover the existing attributes using upcoming attribute values; (c) We
conduct extensive experiments with several baselines and show that PAE is an
effective, flexible and on par or superior (avg 92.5% F1-Score) framework to
existing state-of-the-art for attribute value extraction task.
