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.

翻译：观点摘要旨在从多种主观信息（如产品评论或政治观点）中自动生成摘要。观点摘要的挑战在于呈现分歧甚至相互冲突的观点。我们分析了以往的摘要模型，发现它们倾向于放大极性偏差，强调多数观点而忽略少数观点。为解决这一问题并让摘要器表达双方观点，我们引入了极性校准的概念，其目标是将输出摘要的极性与输入文本的极性对齐。具体而言，我们开发了一种用于极性校准的强化训练方法。该方法将输出摘要与输入文本之间的极性距离作为奖励输入摘要器，并在极性校准、内容保留和语言自然性之间取得平衡。我们在两种观点摘要任务上评估了我们的极性校准模型（PoCa）：总结产品评论和政治观点文章。自动评估和人工评估表明，我们的方法能够减轻输出摘要与输入文本之间的极性失配，同时保持内容语义和语言质量。