  Product attributes are crucial for e-commerce platforms, supporting
applications like search, recommendation, and question answering. The task of
Product Attribute and Value Identification (PAVI) involves identifying both
attributes and their values from product information. In this paper, we
formulate PAVI as a generation task and provide, to the best of our knowledge,
the most comprehensive evaluation of PAVI so far. We compare three different
attribute-value generation (AVG) strategies based on fine-tuning
encoder-decoder models on three datasets. Experiments show that end-to-end AVG
approach, which is computationally efficient, outperforms other strategies.
However, there are differences depending on model sizes and the underlying
language model. The code to reproduce all experiments is available at:
https://github.com/kassemsabeh/pavi-avg
