Keyphrases have found wide ranging application in NLP and IR tasks such as document summarization, indexing, labeling, clustering and classification. In this paper we pose the problem of extracting label specific keyphrases from a document which has document level metadata associated with it namely labels or tags (i.e. multi-labeled document). Unlike other, supervised or unsupervised, methods for keyphrase extraction our proposed methods utilizes both the document's text and label information for the task of extracting label specific keyphrases. We propose two models for this purpose both of which model the problem of extracting label specific keyphrases as a random walk on the document's text graph. We evaluate and report the quality of the extracted keyphrases on a popular multi-label text corpus.
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