![]() Most prominent when the entity is heavily featured in the source article. We find consistentĭifferences, such as stronger associations of a collective US government (i.e.,Īdministration) with Biden than with Trump. Sure, Hugin was a Trump delegate to the 2016 Republican National Convention who gave 100,000 to a PAC backing Trump, another 5,200 directly to the Trump campaign and ran the New Jersey finance. Hugin last year praised Trump on national TV. Sure, Hugin was a Trump delegate to the 2016 Republican National Convention who gave 100,000 to a PAC backing Trump, another 5,200 directly to the Trump campaign and ran the New Jersey. Hugin certainly voted for Donald Trump, running his financial campaign in New Jersey. Resources, and use it to assess biases about Donald Trump and Joe Biden in bothĮxtractive and abstractive summarization models. Hugin was also a supporter of President Donald Trump, donating more than 200,000 to a pro-Trump super PAC and the RNC during the 2016 election. ![]() We develop a computational framework based on political entities and lexical The 64-year-old Menendez defeated Hugin, a former pharmaceutical executive, despite having survived a six-week federal corruption trial in late 2017 and a severe admonishment from the Senate. ![]() Portrayal of politicians in automatically generated summaries of news articles. In this work, we use an entity replacement method to investigate the Download a PDF of the paper titled Characterizing Political Bias in Automatic Summaries: A Case Study of Trump and Biden, by Karen Zhou and Chenhao Tan Download PDF Abstract: Growing literature has shown that powerful NLP systems may encode socialīiases however, the political bias of summarization models remains relatively Hugin was a Trump delegate to the 2016 Republican National Convention, was finance chairman for Trump’s New Jersey campaign and donated 100,000 to a pro-Trump super PAC and more than.
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