  Cherry-picking refers to the deliberate selection of evidence or facts that
favor a particular viewpoint while ignoring or distorting evidence that
supports an opposing perspective. Manually identifying cherry-picked statements
in news stories can be challenging. In this study, we introduce a novel
approach to detecting cherry-picked statements by identifying missing important
statements in a target news story using language models and contextual
information from other news sources. Furthermore, this research introduces a
novel dataset specifically designed for training and evaluating cherry-picking
detection models. Our best performing model achieves an F-1 score of about 89%
in detecting important statements. Moreover, results show the effectiveness of
incorporating external knowledge from alternative narratives when assessing
statement importance.
