TextRank is a variant of PageRank typically used in graphs that represent
documents, and where vertices denote terms and edges denote relations between
terms. Quite often the relation between terms is simple term co-occurrence
within a fixed window of k terms. The output of TextRank when applied
iteratively is a score for each vertex, i.e. a term weight, that can be used
for information retrieval (IR) just like conventional term frequency based term
weights. So far, when computing TextRank term weights over co- occurrence
graphs, the window of term co-occurrence is al- ways ?xed. This work departs
from this, and considers dy- namically adjusted windows of term co-occurrence
that fol- low the document structure on a sentence- and paragraph- level. The
resulting TextRank term weights are used in a ranking function that re-ranks
1000 initially returned search results in order to improve the precision of the
ranking. Ex- periments with two IR collections show that adjusting the vicinity
of term co-occurrence when computing TextRank term weights can lead to gains in
early precision