  We propose a new approach for universal lossless text compression, based on
grammar compression. In the literature, a target string $T$ has been compressed
as a context-free grammar $G$ in Chomsky normal form satisfying $L(G) = \{T\}$.
Such a grammar is often called a \emph{straight-line program} (SLP). In this
paper, we consider a probabilistic grammar $G$ that generates $T$, but not
necessarily as a unique element of $L(G)$. In order to recover the original
text $T$ unambiguously, we keep both the grammar $G$ and the derivation tree of
$T$ from the start symbol in $G$, in compressed form. We show some simple
evidence that our proposal is indeed more efficient than SLPs for certain
texts, both from theoretical and practical points of view.
