Understand clearly the figure: Illustration of a Convolutional Neural Network (CNN) architecture for sentence classification
Answering this in terms of NLP examples is quite hard, remember "All models are wrong, some models are useful." First think of this in an image classification problem context, you want to use a large number of filters to collect a large number of features out of the image, one could detect edges, the other could detect densely coloured areas, one might turn a region to b&amp;w. Extend  a similar logic to text, by using a lot of filters, in this case 128, you are trying to capture a lot of features. For an example like , " I like movies very much", a certain filter might detect that like is a positive word and not a similarity comparison, a certain filter of size 2 might detect very much and detect that it is an expression of degeree. You can go on like that, it will be hard to come up with 128 features but the idea is to get enough features. If you think the number is unreasonable and might lead to overfitting, you can reduce the number and compare your results.       
No, 1- maxpool means that you take the maximum value of the output vector after applying a filter to the input. So it has nothing to do with the longest word but rather choose an element from the output that express the extracted feature to the highest amount.