Can I conduct independent t-test when data is infested with outliers ? and how to interpret the t-statistics?
It's fine to do a t-test on unequal sample size, however, the power wouldn't be as good as equal sample size.

1:) Yes or no. Impossible to say without plotting the outliers. What's more important, can you assume your data be normally distributed? Have you checked the QQ-plot? Have you checked the histogram? Do they look like close to a normal distribution? While the t-test is robust against non-normal data as long as the sample size is sufficient large, your data shouldn't behave too far away from a normal.

When you think about outliers, ask yourself the following questions:


How many outliers? If you have many, t-test is probably not appropriate.
Why the outliers? If it's a random error (you're just unlucky), you could include it in the t-test. If it's a systematic error, stop the test, go back and check your data. 
How do you define the outliers? 
Do those outliers look symmetry? If so, you might assume your sample come from a normal population. You can check the skewness of your data. 


You have to try to understand those outliers to come with up a decision.

2:) You can just explain like "the probability of the difference in means is (or isn't) significant".

3:) You should draw a box-plot for each group.