Automatic identification of general and specific sentences by leveraging discourse annotations

In this paper, we introduce the task of identifying general and specific sentences in news articles. Given the novelty of the task, we explore the feasibility of using existing annotations of discourse relations as training data for a general/specific classifier. The classifier relies on several classes of features that capture lexical and syntactic information, as well as word specificity and polarity. We also validate our results on sentences that were directly judged by multiple annotators to be general or specific. We analyze the annotator agreement on specificity judgements and study the strengths and robustness of features. We also provide a task-based evaluation of our classifier on general and specific summaries written by people. Here we show that the specificity levels predicted by our classifier correlates with the intuitive judgement of specificity employed by people for creating these summaries.