This article proposes a novel framework for representing and measuring local coherence. Central
to this approach is the entity-grid representation of discourse, which captures patterns of entity
distribution in a text. The algorithm introduced in the article automatically abstracts a text
into a set of entity transition sequences and records distributional, syntactic, and referential
information about discourse entities. We re-conceptualize coherence assessment as a learning
task and show that our entity-based representation is well-suited for ranking-based generation
and text classification tasks. Using the proposed representation, we achieve good performance on
text ordering, summary coherence evaluation, and readability assessment.
The source code for this work can be downloaded from the link below.