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.

通过引入极性校准概念，在传达文本信息的同时平衡波动偏向，我们的极性校准模型（PoCa）能够减轻输出摘要与输入文本之间的极性不匹配，并保持内容语义和语言质量。

情感极性校准用于观点摘要

Polarity Calibration for Opinion Summarization

Online platforms, such as Facebook, Twitter, and Reddit, provide users with a
rich set of features for sharing and consuming political information,
expressing political opinions, and exchanging potentially contrary political
views. In such activities, two types of communication spaces naturally emerge:
those dominated by exchanges between politically homogeneous users and those
that allow and encourage cross-cutting exchanges in politically heterogeneous
groups. While research on political talk in online environments abounds, we
know surprisingly little about the potentially varying nature of discussions in
politically homogeneous spaces as compared to cross-cutting communication
spaces. To fill this gap, we use Reddit to explore the nature of political
discussions in homogeneous and cross-cutting communication spaces. In
particular, we develop an analytical template to study interaction and
linguistic patterns within and between politically homogeneous and
heterogeneous communication spaces. Our analyses reveal different behavioral
patterns in homogeneous and cross-cutting communications spaces. We discuss
theoretical and practical implications in the context of research on political
talk online.

本文使用 Reddit 探究了同质和异质政治交流空间的交互和语言模式，并发现同质和异质交流空间之间存在不同的行为模式。