  We introduce Biased TextRank, a graph-based content extraction method
inspired by the popular TextRank algorithm that ranks text spans according to
their importance for language processing tasks and according to their relevance
to an input "focus." Biased TextRank enables focused content extraction for
text by modifying the random restarts in the execution of TextRank. The random
restart probabilities are assigned based on the relevance of the graph nodes to
the focus of the task. We present two applications of Biased TextRank: focused
summarization and explanation extraction, and show that our algorithm leads to
improved performance on two different datasets by significant ROUGE-N score
margins. Much like its predecessor, Biased TextRank is unsupervised, easy to
implement and orders of magnitude faster and lighter than current
state-of-the-art Natural Language Processing methods for similar tasks.
