InstructPTS: Instruction-Tuning LLMs for Product Title Summarization
Besnik Fetahu Zhiyu Chen Oleg Rokhlenko Shervin Malmasi
E-commerce product catalogs contain billions of items. Most products have
lengthy titles, as sellers pack them with product attributes to improve
retrieval, and highlight key product aspects. This results in a gap between
such unnatural products titles, and how customers refer to them. It also limits
how e-commerce stores can use these seller-provided titles for recommendation,
QA, or review summarization.
  Inspired by recent work on instruction-tuned LLMs, we present InstructPTS, a
controllable approach for the task of Product Title Summarization (PTS).
Trained using a novel instruction fine-tuning strategy, our approach is able to
summarize product titles according to various criteria (e.g. number of words in
a summary, inclusion of specific phrases, etc.). Extensive evaluation on a
real-world e-commerce catalog shows that compared to simple fine-tuning of
LLMs, our proposed approach can generate more accurate product name summaries,
with an improvement of over 14 and 8 BLEU and ROUGE points, respectively.