A Simple, Concise, Query-based Approach to News Article Summarization Using Sentence Scoring

With the increasing amount of information being digitized and the growing connectedness of the world, access to news and their intricated information is becoming more vital. Because of this growing need, creating news article summaries is becoming an increasingly important task to allow people to access essential information quickly. However, current summarization approaches require complex, taxing algorithms that cannot be seamlessly adopted for others to implement at the speed that we need. To remedy this, we have designed an elegant approach that allows the utilizing technology to quickly employ a multinomial classifier and sentence scoring of news articles to help with querying and filtering news to allow users to obtain a brief, efficient summary of what the articles entail. The multinomial classifier achieves very effective classification of news articles for summarization. Using various complementary sentence scores, we are able to accurately determine sentences that provide the most informative contents with respect to a user query Q. Through the use of this classification and summarization, we allow information of Q to be readily available. Experimental results verify that our news article summarization approach is effective and efficient in creating high-quality summaries. In addition, the conducted empirical study demonstrates that our summarization approach outperform a significant number of DUC summarizers.