Polarity Calibration for Opinion Summarization (2024)

Abstract: 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.

## introduction opinions are prevalent in various areas, such as social media posts, customer reviews, spoken conversations, argumentative debates, or political matters @xcite . opinion summarization enables automatically generating a brief and informative summary from a large volume of opinions, reviews, or subjective text @xcite @xcite @xcite . the automatic opinion summarization models simplify the extraction of valuable insights from the extensive pool of subjective content, playing a pivotal role in various information access applications, such as digest creation, decision making, product development, or public perception monitoring @xcite @xcite . the challenge of opinion summarization lies in presenting divergent or even conflicting opinions. this contrasts sharply with summarizing objective content such as government reports, scientific research, or legal documents, which typically present factual information without the layer of personal perspectives @xcite @xcite . take summarizing product reviews as an example, customers often express differing opinions about the same product, including both positive and negative viewpoints. the central challenge of subjective summarization is aggregating and presenting these disparate opinions. the critical observation of previously developed summarization models is their tendency to amplify the polarity bias of input text, presenting the majority opinions while ignoring the minority opinions (section 2). in the example of summarizing product reviews, we quantify the polarity scores of input text and output summaries. our findings reveal that when the majority of customers express positive opinions about a product, the summarization models directly trained on overwhelming positive reviews can be easily biased to generate overly positive summaries while neglecting the minority of negative opinions (figure 1 , 4 ). the amplification of polarity bias indicates the limitation in the previous approaches. to address this issue and proportionally express both sides of opinions, we propose the idea of polarity calibration, which aims to align the polarity of output summary with that of input text. in contrast to previous work, we argue that when dealing with conflicting opinions, an intelligent summa- ## polarity bias amplification this section provides a quantitative analysis of previous summarization models, which reveals their tendency to amplify the polarity bias. take product reviews summarization as an example, we aim to examine the polarity of output summary and input reviews. to quantify the polarity, we train a sentiment analysis model on the amazon product reviews dataset @xcite to generate polarity scores. this sentiment analyzer is trained to predict whether a review sentence is positive or negative, and we use the predicted probability of the positive class as the polarity score. the polarity score is a numerical value on a scale from zero to one, with zero indicating extreme negative and one indicating extreme positive. figure 1 illustrate the polarity analysis on the amazon product reviews summarization dataset (amasum) @xcite . the x-axis is the average polarity score of sentences in customer reviews, and y-axis is the average polarity score of sentences in summary. each blue data point represents one product. the comparison between model generated summaries and human annotated summaries reveals the model's inclination to magnify polarity bias. figure ## polarity calibration this section introduces the methodology for polarity calibration, which is illustrated in figure 2 . the polarity calibration is designed in two steps: firstly training a base summarizer with supervised learning, to equip the model with opinion summarization ability, and secondly training a calibrated summarizer through reinforcement learning, with the aim of refining the model's polarity alignment. the calibrated summarizer after polarity calibration is named as poca. ## conclusion this paper focuses on opinion summarization task. we conduct an analysis of previous summarization models, which reveals their tendency to amplify the polarity bias in input text. to mitigate polarity bias and improve polarity alignment between output summary and input text, we introduce the concept of polarity calibration. a reinforcement learning approach is developed for polarity calibration, by designing three rewards for polarity distance, content semantics, and language fluency. experiments demonstrate the effectiveness of our approach in calibrating polarity while preserving content semantics and language quality.