Multi-Objective Forward Reasoning and Multi-Reward Backward Refinement for Product Review Summarization

Libo Sun, Siyuan Wang, Meng Han, Ruofei Lai, Xinyu Zhang, Xuanjing Huang, Zhongyu Wei


Abstract
Product review summarization aims to generate a concise summary based on product reviews to facilitate purchasing decisions. This intricate task gives rise to three challenges in existing work: factual accuracy, aspect comprehensiveness, and content relevance. In this paper, we first propose an FB-Thinker framework to improve the summarization ability of LLMs with multi-objective forward reasoning and multi-reward backward refinement. To enable LLM with these dual capabilities, we present two Chinese product review summarization datasets, Product-CSum and Product-CSum-Cross, for both instruction-tuning and cross-domain evaluation. Specifically, these datasets are collected via GPT-assisted manual annotations from an online forum and public datasets. We further design an evaluation mechanism Product-Eval, integrating both automatic and human evaluation across multiple dimensions for product summarization. Experimental results show the competitiveness and generalizability of our proposed framework in the product review summarization tasks.
Anthology ID:
2024.lrec-main.1043
Volume:
Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
Month:
May
Year:
2024
Address:
Torino, Italia
Editors:
Nicoletta Calzolari, Min-Yen Kan, Veronique Hoste, Alessandro Lenci, Sakriani Sakti, Nianwen Xue
Venues:
LREC | COLING
SIG:
Publisher:
ELRA and ICCL
Note:
Pages:
11944–11955
Language:
URL:
https://preview.aclanthology.org/build-pipeline-with-new-library/2024.lrec-main.1043/
DOI:
Bibkey:
Cite (ACL):
Libo Sun, Siyuan Wang, Meng Han, Ruofei Lai, Xinyu Zhang, Xuanjing Huang, and Zhongyu Wei. 2024. Multi-Objective Forward Reasoning and Multi-Reward Backward Refinement for Product Review Summarization. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 11944–11955, Torino, Italia. ELRA and ICCL.
Cite (Informal):
Multi-Objective Forward Reasoning and Multi-Reward Backward Refinement for Product Review Summarization (Sun et al., LREC-COLING 2024)
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PDF:
https://preview.aclanthology.org/build-pipeline-with-new-library/2024.lrec-main.1043.pdf