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	Aspect and Sentiment Aware Abstractive Review Summarization
	Min Yang; Qiang Qu; Ying Shen; Qiao Liu; Wei Zhao; Jia Zhu
	2018
会议日期	2018
会议地点	New Mexico, USA
英文摘要	Review text has been widely studied in traditional tasks such as sentiment analysis and aspect extraction. However, to date, no work is towards the end-to-end abstractive review summarization that is essential for business organizations and individual consumers to make informed decisions. This work takes the lead to study the aspect/sentiment-aware abstractive review summarization in an end-to-end manner without hand-crafted features and templates by exploring the encoder-decoder framework and multi-factor attentions. Specifically, we propose a mutual attention mechanism to interactively learns the representations of context words, sentiment words and aspect words within the reviews, acted as an encoder. The learned sentiment and aspect representations are incorporated into the decoder to generate aspect/sentiment-aware review summaries via an attention fusion network. In addition, the abstractive summarizer is jointly trained with the text categorization task, which helps learn a category-specific text encoder, locating salient aspect information and exploring the variations of style and wording of content with respect to different text categories. The experimental results on a real-life dataset demonstrate that our model achieves impressive results compared to other strong competitors.
语种	英语
内容类型	会议论文
源URL	[http://ir.siat.ac.cn:8080/handle/172644/14100]
专题	深圳先进技术研究院_数字所
推荐引用方式 GB/T 7714	Min Yang,Qiang Qu,Ying Shen,et al. Aspect and Sentiment Aware Abstractive Review Summarization[C]. 见:. New Mexico, USA. 2018.

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