  Factoid questions are questions that require short fact-based answers.
Automatic generation (AQG) of factoid questions from a given text can
contribute to educational activities, interactive question answering systems,
search engines, and other applications. The goal of our research is to generate
factoid source-question-answer triplets based on a specific domain. We propose
a four-component pipeline, which obtains as input a training corpus of
domain-specific documents, along with a set of declarative sentences from the
same domain, and generates as output a set of factoid questions that refer to
the source sentences but are slightly different from them, so that a
question-answering system or a person can be asked a question that requires a
deeper understanding and knowledge than a simple word-matching. Contrary to
existing domain-specific AQG systems that utilize the template-based approach
to question generation, we propose to transform each source sentence into a set
of questions by applying a series of domain-independent rules (a
syntactic-based approach). Our pipeline was evaluated in the domain of cyber
security using a series of experiments on each component of the pipeline
separately and on the end-to-end system. The proposed approach generated a
higher percentage of acceptable questions than a prior state-of-the-art AQG
system.
