Unsupervised Extractive Opinion Summarization based on Text Simplification and Sentiment Guidance
Authors: Rui Wang, Tian Lan, Yao Liu, Zufeng Wu, piao tong


Reviewer paper summaries: To address the sentiment inconsistency in the extracted summaries, the paper proposes a new method, TSSG, for extractive opinion summarization. Specifically, it first uses a text simplification to simplify the review sentences, then uses a sentiment classification model to identify the sentiment of each sentence. Eventually, the review sentences whose sentiments are closer to the overall sentiment distribution are extracted. The paper then performs experiments on the Amazon and Space dataset. The experiments show that while the ROUGE scores of summaries generated by TSSG are comparable with other baseline, the sentiment distributions of the generated summaries are more close to the sentiment distribution of gold summaries.
Summary of strengths: (1) The authors show that their system can reduce sentiment inconsistency compared to other systems on the Space dataset.


(2) The authors perform comprehensive ablation experiments on two datasets to show the effectiveness of each module.
Summary of weaknesses: (1) The paper should disscuss the relationship of their method with other abstractive opinion summarization methods that use sentiment information. For example, [1,2] use sentiment embedding to generate pseudo summaries, which are also cited in the introduction. More recently, [3] proposes an abstractive opinion summarization method for generating abstractive opinion summaries whose sentiment distributions should be close to the input sentiment distribution. The idea of this paper is very similar to [3].

[1] Amplayo, Reinald Kim, Stefanos Angelidis, and Mirella Lapata. "Unsupervised opinion summarization with content planning." Proceedings of the AAAI conference on artificial intelligence. Vol. 35. No. 14. 2021.

[2] Wang, Ke, and Xiaojun Wan. "TransSum: Translating aspect and sentiment embeddings for self-supervised opinion summarization." Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021. 2021.

[3] Lei, Yuanyuan, et al. "Polarity Calibration for Opinion Summarization." arXiv preprint arXiv:2404.01706 (2024).

(2)  In Sec 5.1.1, the paper only shows that TSSG can improve sentiment consistency on the Space dataset. It would be better if the paper can additionally include experiments on the Amazon dataset. Besides, almost all gold summaries of the Space dataset contain only positive informations. It would be better to perform experiments when gold summaries contain negative information.

(3)  In Table 2,3 and 4, the differences of ROUGE scores between different methods are relatively small. The author should perform statistical tests to show whether the improvement is statiscally significant. Besides, in  `Building' column of Table 4, the RL for TokenCluster is higher than TSSG but TSSG is bolded

(4) The writing of the paper can be improved since therer are some typos in the paper. See Typos Section. Besides, I think the paper can discuss more on the meaning of 'non-concensus' in the introduction.
Comments, suggestions and typos: Line 367: It should be 'We compare TSSG with '

Equation 2 ,5, 6, 7: the notation is inconsistent

Equation 1: BART is generally not refered as LLM.
Overall assessment scores: 2.5