One goal of text mining is to provide readers with automatic methods for quickly finding the key ideas in individual documents and whole corpora. To this effect, we propose a statistically well-founded method for identifying the original ideas that a document contributes to a corpus, focusing on self-referential diachronic corpora such as research publications, blogs, email, and news articles. Our statistical model of passage impact defines (interesting) original content through a combination of impact and novelty, and it can be used to identify the most original passages in a document. Unlike heuristic approaches, this statistical model is extensible and open to analysis. We evaluate the approach on both synthetic and real data, showing that the passage impact model outperforms a heuristic baseline method.