User Review Writing via Interview with Dialogue Systems
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Abstract (arXiv)
User reviews on e-commerce and review sites are crucial for making purchase decisions, although creating detailed reviews is time-consuming and labor-intensive. In this study, we propose a novel use of dialogue systems to facilitate user review creation by generating reviews from information gathered during interview dialogues with users. To validate our approach, we implemented our system using GPT-4 and conducted comparative experiments from the perspectives of system users and review readers. The results indicate that participants who used our system rated their interactions positively. Additionally, reviews generated by our system required less editing to achieve user satisfaction compared to those by the baseline. We also evaluated the reviews from the reader' perspective and found that our system-generated reviews are more helpful than those written by humans. Despite challenges with the fluency of the generated reviews, our method offers a promising new approach to review writing.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 13 of its 36 references that are in the map. First posted on arXiv 7 Mar 2026.
Builds on
13 of 36 references.
References · 13
- 2022Large Language Models are Zero-Shot Reasonerscited by 4,374
- 2019SAMSum Corpus: A Human-annotated Dialogue Dataset for Abstractive Summarizationcited by 535
- 2021QMSum: A New Benchmark for Query-based Multi-domain Meeting Summarizationcited by 365
- 2021MediaSum: A Large-scale Media Interview Dataset for Dialogue Summarizationcited by 104
- 2021DialogLM: Pre-trained Model for Long Dialogue Understanding and Summarizationcited by 72
- 2019Unsupervised Opinion Summarization as Copycat-Review Generationcited by 68
- 2022Interacting with next-phrase suggestions: How suggestion systems aid and influence the cognitive processes of writingcited by 27
- 2019Towards Controllable and Personalized Review Generationcited by 24
- 2021Low-Resource Dialogue Summarization with Domain-Agnostic Multi-Source Pretrainingcited by 19
- 2019Generating Long and Informative Reviews with Aspect-Aware Coarse-to-Fine Decodingcited by 13
- 2020Unsupervised Abstractive Dialogue Summarization for Tete-a-Tetescited by 12
- 2022DIONYSUS: A Pre-trained Model for Low-Resource Dialogue Summarizationcited by 6
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Citing papers per year: 2026: 1.
1 paper.