Abstract
We model products’ reviews to generate comparative responses consisting of positive and negative experiences regarding the product. Specifically, we generate a single-sentence, comparative response from a given positive and a negative opinion. We contribute the first dataset for this task of Comparative Snippet Generation from contrasting opinions regarding a product, and an analysis of performance of a pre-trained BERT model to generate such snippets.- Anthology ID:
- 2022.ecnlp-1.7
- Volume:
- Proceedings of the Fifth Workshop on e-Commerce and NLP (ECNLP 5)
- Month:
- May
- Year:
- 2022
- Address:
- Dublin, Ireland
- Editors:
- Shervin Malmasi, Oleg Rokhlenko, Nicola Ueffing, Ido Guy, Eugene Agichtein, Surya Kallumadi
- Venue:
- ECNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 49–57
- Language:
- URL:
- https://aclanthology.org/2022.ecnlp-1.7
- DOI:
- 10.18653/v1/2022.ecnlp-1.7
- Cite (ACL):
- Saurabh Jain, Yisong Miao, and Min-Yen Kan. 2022. Comparative Snippet Generation. In Proceedings of the Fifth Workshop on e-Commerce and NLP (ECNLP 5), pages 49–57, Dublin, Ireland. Association for Computational Linguistics.
- Cite (Informal):
- Comparative Snippet Generation (Jain et al., ECNLP 2022)
- PDF:
- https://preview.aclanthology.org/proper-vol2-ingestion/2022.ecnlp-1.7.pdf
- Code
- wing-nus/comparative-snippet-generation-dataset
- Data
- SPOT