Tejpalsingh Siledar
ABSTRACT
Opinion summarization is the task of summarizing opinions expressed for an entity or a product. Traditionally, all approaches use customer reviews or ratings to extract opinions and generate summaries. In this work, we propose using four sources of information: product description, product specification, customer reviews, and question-answers for aspect-sentiment-based opinion summarization. We use a transfer-learning approach to fine-tune a pre-trained model to generate summaries. Our experiments demonstrate the importance of all the four information sources resulting in improved quality of summaries. More specifically, product description and customer reviews contribute to detecting aspects and generating aspect-level and general summaries. Product specifications help in enriching the aspects whereas question-answers provide additional information. We make our annotated dataset containing the four information sources publicly available.
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Index Terms
- Aspect-Sentiment-based Opinion Summarization using Multiple Information Sources
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