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        <title>InstructPTS: Instruction-Tuning LLMs for Product Title Summarization</title>
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        <namePart type="given">Shervin</namePart>
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            <title>Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: Industry Track</title>
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    <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.</abstract>
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    <identifier type="doi">10.18653/v1/2023.emnlp-industry.63</identifier>
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%0 Conference Proceedings
%T InstructPTS: Instruction-Tuning LLMs for Product Title Summarization
%A Fetahu, Besnik
%A Chen, Zhiyu
%A Rokhlenko, Oleg
%A Malmasi, Shervin
%Y Wang, Mingxuan
%Y Zitouni, Imed
%S Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: Industry Track
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F fetahu-etal-2023-instructpts
%X 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.
%R 10.18653/v1/2023.emnlp-industry.63
%U https://aclanthology.org/2023.emnlp-industry.63
%U https://doi.org/10.18653/v1/2023.emnlp-industry.63
%P 663-674

Markdown (Informal)

[InstructPTS: Instruction-Tuning LLMs for Product Title Summarization](https://aclanthology.org/2023.emnlp-industry.63) (Fetahu et al., EMNLP 2023)

• InstructPTS: Instruction-Tuning LLMs for Product Title Summarization (Fetahu et al., EMNLP 2023)

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.