Condensed summaries of newspaper articles cater to the modern need for easily digestible content amid shrinking attention spans. However, current summarization systems often produce extracts failing to capture the essence of original articles. Traditional evaluation metrics like ROUGE also provide limited insights into whether key information is preserved in the summaries. To address this, we propose a pipeline to generate high-quality summaries tailored for newspaper articles and evaluate them using a question-answering based metric. Our system segments input newspaper images, extracts text, and generates summaries. We also generate relevant questions from the original articles and use a question-answering model to assess how well the summaries can answer these queries to evaluate summary quality beyond just lexical overlap. Experiments on real-world data show the potential effectiveness of our approach in contrast to conventional metrics. Our framework holds promise for enabling reliable news summary generation and evaluation systems.