# InstructPTS: Instruction-Tuning LLMs for Product Title Summarization

arXiv: https://arxiv.org/abs/2310.16361 · HTML: https://mlsanity.com/paper/2310.16361/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 16 references that are in the map.
- First posted on arXiv 25 Oct 2023.

## Builds on (most cited first, 8 of 8)

- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [CTRL: A Conditional Transformer Language Model for Controllable Generation](https://mlsanity.com/paper/1909.05858/) (2019)
- [Answering Product-Questions by Utilizing Questions from Other Contextually Similar Products](https://mlsanity.com/paper/2105.08956/) (2021)
- [Multi-Modal Generative Adversarial Network for Short Product Title Generation in Mobile E-Commerce](https://mlsanity.com/paper/1904.01735/) (2019)
- [Multi-Source Pointer Network for Product Title Summarization](https://mlsanity.com/paper/1808.06885/) (2018)
- [Automatic Generation of Chinese Short Product Titles for Mobile Display](https://mlsanity.com/paper/1803.11359/) (2018)
- [A Multi-task Learning Approach for Improving Product Title Compression with User Search Log Data](https://mlsanity.com/paper/1801.01725/) (2018)
- [Discriminative Pre-training for Low Resource Title Compression in Conversational Grocery](https://mlsanity.com/paper/2012.06943/) (2020)

## Built on by (most cited first, 9 of 9)

- [A Systematic Survey of Text Summarization: From Statistical Methods to Large Language Models](https://mlsanity.com/paper/2406.11289/) (2024)
- [Text2Chart31: Instruction Tuning for Chart Generation with Automatic Feedback](https://mlsanity.com/paper/2410.04064/) (2024)
- [LLMs with Industrial Lens: Deciphering the Challenges and Prospects -- A Survey](https://mlsanity.com/paper/2402.14558/) (2024)
- [Generative Explore-Exploit: Training-free Optimization of Generative Recommender Systems using LLM Optimizers](https://mlsanity.com/paper/2406.05255/) (2024)
- [No Size Fits All: The Perils and Pitfalls of Leveraging LLMs Vary with Company Size](https://mlsanity.com/paper/2408.01444/) (2024)
- [Length Representations in Large Language Models](https://mlsanity.com/paper/2507.20398/) (2025)
- [Generating Query-Relevant Document Summaries via Reinforcement Learning](https://mlsanity.com/paper/2508.08404/) (2025)
- [LentEx: Generalizable Latent Entity Extraction via Synthetic Data and Instruction-Tuned LLMs](https://mlsanity.com/paper/2609.04511/) (2026)
- [Enhancing News Summarization with ELearnFit through Efficient In-Context Learning and Efficient Fine-Tuning](https://mlsanity.com/paper/2405.02710/) (2024)

## Abstract (arXiv)

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.