Abstract
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.- Anthology ID:
- 2023.emnlp-industry.63
- Volume:
- Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: Industry Track
- Month:
- December
- Year:
- 2023
- Address:
- Singapore
- Editors:
- Mingxuan Wang, Imed Zitouni
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 663–674
- Language:
- URL:
- https://aclanthology.org/2023.emnlp-industry.63
- DOI:
- 10.18653/v1/2023.emnlp-industry.63
- Cite (ACL):
- Besnik Fetahu, Zhiyu Chen, Oleg Rokhlenko, and Shervin Malmasi. 2023. InstructPTS: Instruction-Tuning LLMs for Product Title Summarization. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: Industry Track, pages 663–674, Singapore. Association for Computational Linguistics.
- Cite (Informal):
- InstructPTS: Instruction-Tuning LLMs for Product Title Summarization (Fetahu et al., EMNLP 2023)
- PDF:
- https://preview.aclanthology.org/proper-vol2-ingestion/2023.emnlp-industry.63.pdf