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.

通过对商品标题进行指导性调优，我们提出了 InstructPTS，一种能够按照不同标准（如摘要中的字数、特定短语的包含等）总结产品标题的可控方法。在真实世界的电子商务目录上进行的大量评估表明，与仅使用 LLMs 进行简单调优相比，我们所提出的方法能够生成更准确的产品名称摘要，BLEU 和 ROUGE 分别提高了 14 个和 8 个分数。