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.

We now have incrementally-grown databases of text documents ranging back for over a decade in areas ranging from personal email, to news-articles and conference proceedings. While accessing individual documents is easy, methods for overviewing and understanding these collections as a whole are lacking in number and in scope. In this paper, we address one such global analysis task, namely the problem of automatically uncovering how ideas spread through the collection over time. We refer to this problem as Information Genealogy. In contrast to bibliometric methods that are limited to collections with explicit citation structure, we investigate content-based methods requiring only the text and timestamps of the documents. In particular, we propose a language-modeling approach and a likelihood ratio test to detect influence between documents in a statistically well-founded way. Furthermore, we show how this method can be used to infer citation graphs and to identify the most influential documents in the collection. Experiments on the NIPS conference proceedings and the Physics ArXiv show that our method is more effective than methods based on document similarity.