  This paper presents a novel research problem on joint discovery of
commonalities and differences between two individual documents (or document
sets), called Comparative Document Analysis (CDA). Given any pair of documents
from a document collection, CDA aims to automatically identify sets of quality
phrases to summarize the commonalities of both documents and highlight the
distinctions of each with respect to the other informatively and concisely. Our
solution uses a general graph-based framework to derive novel measures on
phrase semantic commonality and pairwise distinction}, and guides the selection
of sets of phrases by solving two joint optimization problems. We develop an
iterative algorithm to integrate the maximization of phrase commonality or
distinction measure with the learning of phrase-document semantic relevance in
a mutually enhancing way. Experiments on text corpora from two different
domains---scientific publications and news---demonstrate the effectiveness and
robustness of the proposed method on comparing individual documents. Our case
study on comparing news articles published at different dates shows the power
of the proposed method on comparing document sets.
