Which questions should I answer? Salience Prediction of Inquisitive Questions

Inquisitive questions -open-ended, curiositydriven questions people ask as they read -are an integral part of discourse processing (Van Kuppevelt, 1995; Onea, 2016; Kehler and Rohde, 2017) and comprehension (Prince, 2004). Recent work in NLP has taken advantage of question generation capabilities of LLMs to enhance a wide range of applications. But the space of inquisitive questions is vast: many potential questions can be evoked from a given context. So which of those should be prioritized to find answers? Linguistic theories, unfortunately, have not yet provided an answer. This paper presents QSALIENCE, a salience predictor of inquisitive questions. QSALIENCE is instruction-tuned over our dataset of linguistannotated salience scores of 1,766 (context, question) pairs. A question scores high on salience if answering it would greatly enhance the understanding of the text (Van Rooy, 2003) . We show that highly salient questions are empirically more likely to be answered in the same article, bridging potential questions (Onea, 2016) with Questions Under Discussion (Roberts, 2012) . We further validate our findings by showing that answering salient questions is an indicator of summarization quality in news.