  Content-dense news report important factual information about an event in
direct, succinct manner. Information seeking applications such as information
extraction, question answering and summarization normally assume all text they
deal with is content-dense. Here we empirically test this assumption on news
articles from the business, U.S. international relations, sports and science
journalism domains. Our findings clearly indicate that about half of the news
texts in our study are in fact not content-dense and motivate the development
of a supervised content-density detector. We heuristically label a large
training corpus for the task and train a two-layer classifying model based on
lexical and unlexicalized syntactic features. On manually annotated data, we
compare the performance of domain-specific classifiers, trained on data only
from a given news domain and a general classifier in which data from all four
domains is pooled together. Our annotation and prediction experiments
demonstrate that the concept of content density varies depending on the domain
and that naive annotators provide judgement biased toward the stereotypical
domain label. Domain-specific classifiers are more accurate for domains in
which content-dense texts are typically fewer. Domain independent classifiers
reproduce better naive crowdsourced judgements. Classification prediction is
high across all conditions, around 80%.
