What does it mean when the coefficient of the squared term is negative in regression? I am reading a research paper which models a regression model where the returns are regressed on the number of ad exposures.

the equation looks something like this:

$Returns = beta_1*nExp + beta_2*nExp^2$

nExp: The number of times the user looks at the ad.

Returns: The revenue from the user


The result is that: $beta_1$ is positive (and statistically significant), which means that the ad exposures has a $+ve$ relation with the revenue.

However, $beta_2$ is negative. And the research paper says: 


  term for the square of the number of exposures, which has a negative
  coefficient suggesting diminishing returns to ad exposure.


What does it really mean? What does it mean when the term's coefficient is $+ve$ but the coefficient of it's squared term in the equation is $-ve$?
In this context probably the plain English way to put it is that 'returns increase with additional exposure to the ad, but there is a tapering effect at the upper end of exposures. Looking at this picture of a parabola here (i.e a graph of y= -ax^2+bx+c):

Most likely your returns data are between 0 and the peak so you don't actually see  a decline, just a reduction in the rate of increase.