  Opinion summarization is automatically generating summaries from a variety of
subjective information, such as product reviews or political opinions. The
challenge of opinions summarization lies in presenting divergent or even
conflicting opinions. We conduct an analysis of previous summarization models,
which reveals their inclination to amplify the polarity bias, emphasizing the
majority opinions while ignoring the minority opinions. To address this issue
and make the summarizer express both sides of opinions, we introduce the
concept of polarity calibration, which aims to align the polarity of output
summary with that of input text. Specifically, we develop a reinforcement
training approach for polarity calibration. This approach feeds the polarity
distance between output summary and input text as reward into the summarizer,
and also balance polarity calibration with content preservation and language
naturality. We evaluate our Polarity Calibration model (PoCa) on two types of
opinions summarization tasks: summarizing product reviews and political
opinions articles. Automatic and human evaluation demonstrate that our approach
can mitigate the polarity mismatch between output summary and input text, as
well as maintain the content semantic and language quality.
