  Conversational AI assistants are becoming popular and question-answering is
an important part of any conversational assistant. Using relevant utterances as
features in question-answering has shown to improve both the precision and
recall for retrieving the right answer by a conversational assistant. Hence,
utterance generation has become an important problem with the goal of
generating relevant utterances (sentences or phrases) from a knowledge base
article that consists of a title and a description. However, generating good
utterances usually requires a lot of manual effort, creating the need for an
automated utterance generation. In this paper, we propose an utterance
generation system which 1) uses extractive summarization to extract important
sentences from the description, 2) uses multiple paraphrasing techniques to
generate a diverse set of paraphrases of the title and summary sentences, and
3) selects good candidate paraphrases with the help of a novel candidate
selection algorithm.
