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.
REF study the aspect/sentiment-aware abstractive review sum-marization in an end-to-end manner.
Aspect and Sentiment Aware Abstractive Review Summarization
COLING
Computer Science