  The rapidly growing amount of data that scientific content providers should
deliver to a user makes them create effective recommendation tools. A title of
an article is often the only shown element to attract people's attention. We
offer an approach to automatic generating titles with various levels of
informativeness to benefit from different categories of users. Statistics from
ResearchGate used to bias train datasets and specially designed post-processing
step applied to neural sequence-to-sequence models allow reaching the desired
variety of simplified titles to gain a trade-off between the attractiveness and
transparency of recommendation.
