A content-driven search-keyword (SK-) Suggester for keyword-based search in digital libraries is proposed. Suggesting search terms while the user is entering search terms is helpful for constructing correctly-typed and focused search terms for digita l library queries. The proposed SK-Suggester is bas ed on pre-analyzing step of the publication collection to be searched. The pre-analysis step consists of the following. (i) We parse the document collection usi ng a Link-Grammar parser, a syntactic parser of English , next, (ii) we group publications based on their research topics, (iii) after that, the parser output is used to build a hierarchical structure of simple an d compound tokens to be used to suggest search term s. In order to sort the suggested terms, we use the Te xtRank algorithm, a text summarization tool, to assi gn topic-sensitive scores to the simple and compound t okens. The identified research topics are used to h elp user entering focused search terms prior to the act ual search query execution. The topic-sensitive TextRank scores are further refined to incorporate t he user’s citation behavior model proposed in [Bani- Ahmad, S., Ozsoyoglu, T. 2009]. We experimentally show that the proposed framework promises a more scalable, high quality, and user- friendly SK-Suggester when compared to its competitors. We validate our proposal experimentally using a subset of the ACM SIGMOD Anthology digital library as a testbed, and by employing the research- pyramid model to identify the research topics.