P^3SUM: Preserving Author's Perspective in News Summarization with Diffusion Language Models
arxiv(2023)
Abstract
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
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
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.
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