Summarization techniques strive to create a concise summary that conveys the essential information from a given document. However, these techniques are often inadequate for summarizing longer documents containing multiple pages of semantically complex content with various topics. Hence, in this work, we present a Topic-Conditional Summarization (TCS) method, that produces different summaries each conforming to a different topic. TCS is an unsupervised method and does not require ground truth summaries. The proposed algorithm adapts the TextRank paradigm and enhances it with a language model specialized in a set of documents and their topics. Extensive evaluations across multiple datasets indicate that our method improves upon other alternatives by a sizeable margin.