Using Semantic and Context Features for Answer Summary Extraction

Abstract

We investigate the effectiveness of using semantic and context features for extracting document summaries that are designed to contain answers for non-factoid queries. The summarization methods are compared against state-of-the-art factoid question answering and query-biased summarization techniques. The accuracy of generated answer summaries are evaluated using ROUGE as well as sentence ranking measures, and the relationship between these measures are further analyzed. The results show that semantic and context features give significant improvement to the state-of-the-art techniques.