A Comparative Study of the Impact of Statistical and Semantic Features in the Framework of Extractive Text Summarization

This paper evaluates the impact of a set of statistical and semantic features as applied to the task of extractive summary generation for English. This set includes word frequency, inverse sentence frequency, inverse term frequency, corpus-tailored stopwords, word senses, resolved anaphora and textual entailment. The obtained results show that not all of the selected features equally benefit the performance. The term frequency combined with stopwords filtering is a highly competitive baseline that nevertheless can be topped when semantic information is included. However, in the selected experiment environment the recall values improved less than expected and we are interested in further investigating the reasons.