Growing polarization of the news media has been blamed for fanning disagreement, controversy and even violence . Early identification of polarized topics is thus an urgent matter that can help mitigate conflict . However, accurate measurement of polarization is still an open research challenge . To address this gap, we propose Partisanship-aware Contextualized Topic Embeddings (PaCTE), a method to automatically detect polarized topics from partisan news sources . Specifically, we represent the ideology of a news source on a topic by corpus-contextualized topic embedding utilizing a language model that has been finetuned on recognizing partisanship of the news articles, and measure the polarization between sources using cosine similarity . We apply our method to a corpus of news about COVID-19 pandemic . Extensive experiments on different news sources and topics demonstrate the effectiveness of our method to precisely capture the topical polarization and alignment between different news sources . To help clarify and validate results, we explain the polarization using the Moral Foundation Theory.