This paper presents a new unsupervised approach to generating ultra-concise summaries of opinions. We formulate the problem of generating such a micropinion summary as an optimization problem, where we seek a set of concise and non-redundant phrases that are readable and represent key opinions in text. We measure representativeness based on a modified mutual information function and model readability with an n-gram language model. We propose some heuristic algorithms to efficiently solve this optimization problem.
This survey zooms into recent research in the area of opinion summarization, which is related to generating effective summaries of opinions so that users can get a quick understanding of the underlying sentiments. Since there are various formats of summaries, the survey breaks down the approaches into the commonly studied aspect-based summariztion and non-aspect based ones (which includes visualization, contrastive summarization and text summarization). This survey also has a listing of opinion related dataset and available demos.
We present a novel graph-based summarization framework (Opinosis) that generates concise abstractive summaries of highly redundant opinions. Evaluation results on summarizing user reviews show that Opinosis summaries have better agreement with human summaries compared to the baseline extractive method. The summaries are readable, reasonably well-formed and are informative enough to convey the major opinions.