# Extraction of Atypical Aspects from Customer Reviews: Datasets and Experiments with Language Models

arXiv: https://arxiv.org/abs/2311.02702 · HTML: https://mlsanity.com/paper/2311.02702/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 50 references that are in the map.
- First posted on arXiv 5 Nov 2023.

## Builds on (most cited first, 12 of 12)

- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [Finetuned Language Models Are Zero-Shot Learners](https://mlsanity.com/paper/2109.01652/) (2021)
- [DeBERTa: Decoding-enhanced BERT with Disentangled Attention](https://mlsanity.com/paper/2006.03654/) (2020)
- [A Neural Attention Model for Abstractive Sentence Summarization](https://mlsanity.com/paper/1509.00685/) (2015)
- [Do Prompt-Based Models Really Understand the Meaning of their Prompts?](https://mlsanity.com/paper/2109.01247/) (2021)
- [Chat-REC: Towards Interactive and Explainable LLMs-Augmented Recommender System](https://mlsanity.com/paper/2303.14524/) (2023)
- [PALR: Personalization Aware LLMs for Recommendation](https://mlsanity.com/paper/2305.07622/) (2023)
- [Opening up ChatGPT: Tracking openness, transparency, and accountability in instruction-tuned text generators](https://mlsanity.com/paper/2307.05532/) (2023)
- [RevCore: Review-augmented Conversational Recommendation](https://mlsanity.com/paper/2106.00957/) (2021)
- [ASPECTNEWS: Aspect-Oriented Summarization of News Documents](https://mlsanity.com/paper/2110.08296/) (2021)
- [PURS: Personalized Unexpected Recommender System for Improving User Satisfaction](https://mlsanity.com/paper/2106.02771/) (2021)

## Abstract (arXiv)

A restaurant dinner may become a memorable experience due to an unexpected aspect enjoyed by the customer, such as an origami-making station in the waiting area. If aspects that are atypical for a restaurant experience were known in advance, they could be leveraged to make recommendations that have the potential to engender serendipitous experiences, further increasing user satisfaction. Although relatively rare, whenever encountered, atypical aspects often end up being mentioned in reviews due to their memorable quality. Correspondingly, in this paper we introduce the task of detecting atypical aspects in customer reviews. To facilitate the development of extraction models, we manually annotate benchmark datasets of reviews in three domains - restaurants, hotels, and hair salons, which we use to evaluate a number of language models, ranging from fine-tuning the instruction-based text-to-text transformer Flan-T5 to zero-shot and few-shot prompting of GPT-3.5.