A dependency relation-based method to identify attributive relations and its application in text summarization

In this paper, we propose a domain and genre-independent approach to identify the discourse relation called attributive, included in Grimes’ relation list [7]. An attributive relation provides details about an entity or an event or can be used to illustrate a particular feature about a concept or an entity. Since attributive relations describe attributes or features of an object or an event, they are often used in text summarization (e.g. [2]) and question answering systems (e.g. [12]). However, to our knowledge, no previous work has focused on tagging attributive relations automatically. We propose an automatic domain and genre-independent approach to tag attributive relations by utilizing dependency relations of words based on dependency grammars [3]. In this paper, we also show how attributive relations can be utilized in text summarization. By using a subset of the BLOG06 corpus, we have evaluated the accuracy of our attributive classifier and compared it to a baseline and human performance using precision, recall, and F-Measure. The evaluation results show that our approach compares favorably with human performance.