P^3SUM: Preserving Author's Perspective in News Summarization with Diffusion Language Models
It addresses the issue of misrepresenting authors' intents in summarization for news readers and AI applications, presenting a novel approach but focusing on a specific domain.
The paper tackles the problem of preserving authors' political perspectives in news summarization, finding that existing approaches alter stances in over 50% of summaries, and proposes P^3SUM, which improves stance preservation success rate by up to 13.7% compared to state-of-the-art systems.
In this work, we take a first step towards designing summarization systems that are faithful to the author's intent, not only the semantic content of the article. Focusing on a case study of preserving political perspectives in news summarization, we find that existing approaches alter the political opinions and stances of news articles in more than 50% of summaries, misrepresenting the intent and perspectives of the news authors. We thus propose P^3SUM, a diffusion model-based summarization approach controlled by political perspective classifiers. In P^3SUM, the political leaning of a generated summary is iteratively evaluated at each decoding step, and any drift from the article's original stance incurs a loss back-propagated to the embedding layers, steering the political stance of the summary at inference time. Extensive experiments on three news summarization datasets demonstrate that P^3SUM outperforms state-of-the-art summarization systems and large language models by up to 13.7% in terms of the success rate of stance preservation, with competitive performance on standard metrics of summarization quality. Our findings present a first analysis of preservation of pragmatic features in summarization, highlight the lacunae in existing summarization models -- that even state-of-the-art models often struggle to preserve author's intents -- and develop new summarization systems that are more faithful to author's perspectives.