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| Query-oriented Opinion Summarization Model Using Debatepedia as Datasource |
| Yu Chuanming1, Zheng Zhiliang1,2, Zhu Xingyu1, An Lu3 |
1.School of Information and Safety Engineering, Zhongnan University of Economics and Law, Wuhan 430073
2.McKelvey School of Engineering, Washington University in St. Louis , St. Louis, Missouri 63130
3.School of Information Management, Wuhan University, Wuhan 430072 |
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Abstract This study systematically studies query-oriented opinion summarization, aiming to construct a query-oriented opinion summarization framework to explore the impact of different text summarization methods on the opinion summarization. Considering sentiment orientation and the similarity between the sentence and query, we extract the sentences from the original documents and employ neural networks and word embeddings to achieve abstractive summarization. The query-oriented opinion summarization framework is then constructed upon this. We crawl the topics and arguments to build the experimental opinion summarization dataset from the Debatepedia websites and apply the proposed method to the dataset to validate its effect. The experimental results show that on this dataset, the summaries generated by the extractive method are of higher quality; the highest average ROUGE score, deep semantic similarity score, and emotional score are 6.58%, 1.79%, and 11.52% higher than the generative method, and 8.33%, 2.80%, and 13.86% higher than the combined method, respectively. Furthermore, the evaluation indicator deep sentence similarity and the sentiment score proposed in this study can better evaluate the effects of the query-oriented opinion summarization model. The research results are of great significance for improving the effects of query-oriented opinion summarization and promoting the application of the opinion summarization model in the field of information science.
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Received: 10 October 2019
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