Opinion Summarization by Weak-Supervision from Mix-structured Data

Opinion summarization of multiple reviews suffers from the lack of reference summaries for training. Most previous approaches construct multiple reviews and their summary based on textual similarities between reviews, resulting in information mismatch between the review input and the summary. In this paper, we convert each review into a mix of structured and unstructured data, which we call opinion-aspect pairs (OAs) and implicit sentences (ISs). We propose a new method to synthesize training pairs of such mix-structured data as input and the textual summary as output, and design a summarization model with OA encoder and IS encoder. Experiments show that our approach outperforms previous methods on Yelp, Amazon and RottenTomatos datasets.