  Searching for health information online is becoming customary for more and
more consumers every day, which makes the need for efficient and reliable
question answering systems more pressing. An important contributor to the
success rates of these systems is their ability to fully understand the
consumers' questions. However, these questions are frequently longer than
needed and mention peripheral information that is not useful in finding
relevant answers. Question summarization is one of the potential solutions to
simplifying long and complex consumer questions before attempting to find an
answer. In this paper, we study the task of abstractive summarization for
real-world consumer health questions. We develop an abstractive question
summarization model that leverages the semantic interpretation of a question
via recognition of medical entities, which enables the generation of
informative summaries. Towards this, we propose multiple Cloze tasks (i.e. the
task of filing missing words in a given context) to identify the key medical
entities that enforce the model to have better coverage in question-focus
recognition. Additionally, we infuse the decoder inputs with question-type
information to generate question-type driven summaries. When evaluated on the
MeQSum benchmark corpus, our framework outperformed the state-of-the-art method
by 10.2 ROUGE-L points. We also conducted a manual evaluation to assess the
correctness of the generated summaries.
