Abstract: The opinion is a written form of consumer evaluation and feedback on a product.Opinion summarization refers to extracting and compressing a review to create a concise text that summarizes the information contained in the review.Currently,most opinion summarization tasks focus solely on the review text,without considering the evaluation object information within the review,such as aspects,opinion phrases,and sentiment polarities.Therefore,this paper proposes an opinion summarization me-thod based on the T5 model(Text-to-Text Transfer Transformer) incorporating the evaluation object information.The method first utilizes the T5 model to represent the task of opinion summarization.It learns contextual information from the review using the attention mechanism and generates a summary that encapsulates the core semantics.Then,it extracts the evaluation object information from the summary as an auxiliary task of opinion summarization.Finally,the model parameters are fine-tuned using a limited sample of data,which further enhances the summary generation process,resulting in a high-quality summary.Experimental results on both the hotel review dataset SPACE and the product review dataset OPOSUM+,show that the proposed method has a significant improvement in the ROUGE evaluation metrics compared to the baseline models.

Key words: Opinion summarization, T5 model, Evaluation object information, Limited sample data, Attention mechanism